Time series is different from a regular regression problem because it is time dependent. The basic assumption of a linear regression that the observations are independent doesn’t hold in this case. Along with an increasing or decreasing trend, most time series have some form of seasonality trends, i.e. variations specific to a particular time frame. Analysis of a time series is important because it is the preparatory step before you develop a forecast of the series. It involves understanding various aspects about the inherent nature of the series so that you are better informed to create meaningful and accurate forecasts.

In this blog post we’ll examine some common techniques used in time series analysis by applying them to a data set containing daily opening values for the United Health Group from 2016 up to present day.

UnitedHealth Group Incorporated is an American for-profit managed health care company based in Minnetonka, Minnesota. It offers health care products and insurance services. It is the largest healthcare company in the world by revenue, with 2019 revenue of $242.2 billion. The company is ranked 6th on the 2019 Fortune 500.

## Model Based Forecasting includes¶

#### Classical & Statistical Learning¶

- Autoregressive integrated moving average (ARIMA)
- Exponential smoothing methods (e.g. Holt-Winters)
- Theta

When the underlying mechanisms are not known or are too complicated, e.g., the stock market, or not fully known, e.g., retail sales, it is usually better to apply a simple statistical model. Popular classical methods that belong to this category include ARIMA (autoregressive integrated moving average), exponential smoothing methods, such as Holt-Winters, and the Theta method.

#### Machine Learning¶

- Recurrent neural networks (RNN)
- Quantile regression forest (QRF)
- Gradient boosting trees (GBM)
- Support vector regression (SVR)
- Gaussian Process regression (GP)

Machine learning models are of a black-box type and are used when interpretability is not a requirement. Both, classical and ML methods are not that different from each other, but distinguished by whether the models are more simple and interpretable or more complex and flexible. In practice. classical statistical algorithms tend to be much quicker and easier-to-use.

## Framework¶

### Step 1: Visualize the Time Series¶

It is essential to analyze the trends prior to building any kind of time series model. The details we are interested in pertains to any kind of trend, seasonality or random behaviour in the series.

### Step 2: Stationarize the Series¶

Once we know the patterns, trends, cycles and seasonality , we can check if the series is stationary or not. Dickey – Fuller is one of the popular test to check the same. If the series is non-stationary, we can use one of the commonly used techniques to make a time series stationary:

- Detrending : Here, we simply remove the trend component from the time series. For instance, the equation of my time series is:

x(t) = (mean + trend * t) + error

We’ll simply remove the part in the parentheses and build model for the rest.

- Differencing : This is the commonly used technique to remove non-stationarity. Here we try to model the differences of the terms and not the actual term. For instance,

x(t) – x(t-1) = ARMA (p , q)

This differencing is called as the Integration part in AR(I)MA. Now, we have three parameters

p : AR (AutoRegressive = the model takes advantage of the connection between a predefined number of lagged observations and the current one.)

d : I (Integrated = differencing between raw observations (eg. subtracting observations at different time steps).)

q : MA (Moving Average = the model takes advantage of the relationship between the residual error and the observations.)

- Seasonality : Seasonality can easily be incorporated in the ARIMA model directly.

### Step 3: Find Optimal Parameters¶

The parameters p,d,q can be found using ACF and PACF plots. An addition to this approach is can be, if both ACF and PACF decreases gradually, it indicates that we need to make the time series stationary and introduce a value to “d”.

### Step 4: Build ARIMA Model¶

With the parameters in hand, we build ARIMA model. The value found in the previous section might be an approximate estimate and we need to explore more (p,d,q) combinations. The one with the lowest BIC and AIC should be our choice. We can also try some models with a seasonal component. Just in case, we notice any seasonality in ACF/PACF plots.

### Step 5: Make Predictions¶

Once we have the final ARIMA model, we are ready to make predictions on the future time points. We can also visualize the trends to cross validate if the model works fine.

### References for this post:¶

- https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/
- https://www.analyticsvidhya.com/blog/2015/12/complete-tutorial-time-series-modeling/
- https://medium.com/analytics-vidhya/preprocessing-for-time-series-forecasting-3a331dbfb9c2
- http://www.gregreda.com/2020/02/16/feature-engineering-with-time-gaps/
- https://www.machinelearningplus.com/time-series/time-series-analysis-python/
- https://www.kdnuggets.com/2019/08/stationarity-time-series-data.html
- http://bashtage.github.io/arch/doc/index.html
- https://www.alphavantage.co/documentation/
- https://towardsdatascience.com/stock-market-analysis-using-arima-8731ded2447a

In [38]:

import pandas as pd import numpy as np import seaborn as sns # advanced vizs import matplotlib.pyplot as plt from matplotlib.pyplot import figure from matplotlib.pyplot import suptitle import matplotlib.style as style from IPython.display import display, HTML # statistics from statsmodels.distributions.empirical_distribution import ECDF # time series analysis from statsmodels.tsa.seasonal import seasonal_decompose from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from statsmodels.tsa.stattools import adfuller from arch.unitroot import KPSS, ZivotAndrews, VarianceRatio from statsmodels.tsa.arima_model import AR, ARMA, ARIMA import warnings %matplotlib inline %config InlineBackend.figure_format = 'png' #set 'png' here when working on notebook plt.style.use('bmh') warnings.filterwarnings('ignore') pd.set_option('display.width',100, 'display.max_rows',20, 'display.max_columns',9,'max_colwidth',100)

## Download Data¶

In [2]:

from datetime import datetime import pandas_datareader.data as web import configparser settings = configparser.ConfigParser() settings.read('../data/processed/config.ini') api_key = settings.get('AlphaVantage', 'api_key') start = datetime(2016, 1, 1) end = datetime.now() f = web.DataReader("UNH", "av-daily", start=start, end=end, api_key= api_key)

In [3]:

f.head()

Out[3]:

open | high | low | close | volume | |
---|---|---|---|---|---|

2016-01-04 | 116.91 | 116.91 | 114.525 | 116.46 | 4990855 |

2016-01-05 | 116.72 | 117.89 | 116.210 | 116.68 | 2816727 |

2016-01-06 | 115.78 | 116.65 | 114.500 | 115.49 | 2677303 |

2016-01-07 | 113.63 | 114.09 | 111.420 | 112.09 | 5276916 |

2016-01-08 | 112.35 | 113.00 | 109.760 | 110.16 | 4422365 |

## Select the Opening Price¶

In [4]:

df = f['open'].to_frame().reset_index() df.columns =['index','open'] df['index'] = pd.to_datetime(df['index']) df.set_index("index", inplace=True)

## Plot¶

In [5]:

f, (ax1) = plt.subplots(1, figsize = (12, 6)) # preparation: input should be float type plt.plot(df) plt.show()

### Resample¶

In [6]:

f, (ax1) = plt.subplots(1, figsize = (12, 6)) # Weekly Resample df1 = df['open'].resample('W').sum().reset_index() plt.plot(df1['index'],df1['open']) plt.show()

### Patterns in a time series¶

Any time series may be split into the following components:

Base Level + Trend + Seasonality + Error

A trend is observed when there is an increasing or decreasing slope observed in the time series. Whereas seasonality is observed when there is a distinct repeated pattern observed between regular intervals due to seasonal factors. It could be because of the month of the year. It happens when the rise and fall pattern in the series does not happen in fixed calendar-based intervals. However, It is not mandatory that all time series must have a trend and/or seasonality. A time series may not have a distinct trend but have a seasonality. The opposite can also be true. If the patterns are not of fixed calendar based frequencies, then it is cyclic. Because, unlike the seasonality, cyclic effects are typically influenced by the business and other socio-economic factors. Furthermore, care should be taken to not confuse ‘cyclic’ effect with ‘seasonal’ effect.

#### How to diffentiate between a ‘cyclic’ vs ‘seasonal’?¶

(Additive and multiplicative time series)

Depending on the nature of the trend and seasonality, a time series can be modeled as an additive or multiplicative, wherein, each observation in the series can be expressed as either a sum or a product of the components:

Additive time series: Value = Base Level + Trend + Seasonality + Error

Multiplicative Time Series: Value = Base Level x Trend x Seasonality x Error In [7]:

def pattern_series(timeseries, model, freq): f, (ax1) = plt.subplots(1, figsize = (12, 9)) decomposition = seasonal_decompose(timeseries, model=model, freq = freq) trend = decomposition.trend seasonal = decomposition.seasonal residual = decomposition.resid plt.suptitle(model) plt.subplot(411) plt.plot(timeseries, label='Original') plt.legend(loc='best') plt.subplot(412) plt.plot(trend, label='Trend') plt.legend(loc='best') plt.subplot(413) plt.plot(seasonal,label='Seasonality') plt.legend(loc='best') plt.subplot(414) plt.plot(residual, label='Residuals') plt.legend(loc='best') plt.tight_layout() plt.show()

### Decomposing¶

In this approach, both trend and seasonality are modeled separately and the remaining part of the series is returned. In [8]:

### Multiplicative pattern_series(df['open'], 'multiplicative', 52)

In [9]:

### Additive pattern_series(df['open'],'additive', 52)

Here we can see that the trend, seasonality are separated out from data. If we look at the residuals of the multiplicative decomposition closely, it has no pattern left over. However, the addtive decomposition looks quite random which is good. Additive decomposition should be preferred for this particular series.

Note: Setting extrapolate_trend=’freq’ takes care of any missing values in the trend and residuals at the beginning of the series.

The numerical output of the trend, seasonal and residual components are stored in the decomposition output itself. We can extract them and put it in a dataframe. In [10]:

# Extract the Components ---- # Actual Values = Sum of (Seasonal + Trend + Resid) decomposition = seasonal_decompose(df['open'], model='additive', freq = 52) df_reconstructed = pd.concat([decomposition.seasonal, decomposition.trend, decomposition.resid, decomposition.observed], axis=1) df_reconstructed.columns = ['seas', 'trend', 'resid', 'actual_values'] df_reconstructed.head()

Out[10]:

seas | trend | resid | actual_values | |
---|---|---|---|---|

index | ||||

2016-01-04 | 1.591094 | NaN | NaN | 116.91 |

2016-01-05 | 1.208426 | NaN | NaN | 116.72 |

2016-01-06 | 0.394594 | NaN | NaN | 115.78 |

2016-01-07 | 1.002825 | NaN | NaN | 113.63 |

2016-01-08 | 0.330801 | NaN | NaN | 112.35 |

## Lag Plots¶

A Lag plot is a scatter plot of a time series against a lag of itself. It is normally used to check for autocorrelation. If there is any pattern existing in the series like the one we see below, the series is autocorrelated. If there is no such pattern, the series is likely to be random white noise. In [11]:

# Lag Plot f, (ax1) = plt.subplots(1, figsize = (9, 6)) pd.plotting.lag_plot(df['open'], lag = 7)

Out[11]:

<matplotlib.axes._subplots.AxesSubplot at 0x2713cbc4898>

## Stationarity¶

We cannot build a time series model unless our time series is stationary. A stationary series is one where the values of the series is not a function of time. It’s statistical properties such as mean, variance remain constant over time. Autocorrelation of the series is nothing but the correlation of the series with its previous values.

### Why make a non-stationary series stationary before forecasting?¶

An important reason is, autoregressive forecasting models are essentially linear regression models that utilize the lag(s) of the series itself as predictors. We know that linear regression works best if the predictors (X variables) are not correlated against each other. So, stationarizing the series solves this problem since it removes any persistent autocorrelation, thereby making the predictors(lags of the series) in the forecasting models nearly independent. Stationarizing the data is a unique aspect of ARIMA models – it helps to determine the parameters of the ARIMA model. If the residuals are stationary after being fed through a linear model, then the Gauss-Markov theorem guarantees us that we have found the best unbiased linear estimator (BLUE) of the data. Another way to think about this is that, if we see that the residuals are not stationary, then there is probably some pattern in the data that we should be able to incorporate into our model such that the residuals become stationary.

It’s also important that the dataset is stationary, otherwise we run the risk of finding spurious correlations. A common example is the relationship between number of TVs per person and life expectancy. It’s not likely that there’s an actual causal relationship there. Rather, there could be a third variable that’s driving both (wealth, say).

### What is the difference between white noise and a stationary series?¶

Like a stationary series, the white noise is also not a function of time, that is its mean and variance does not change over time. But the difference is, the white noise is completely random with a mean of 0. In white noise there is no pattern whatsoever. If we consider the sound signals in an FM radio as a time series, the blank sound you hear between the channels is white noise. Mathematically, a sequence of completely random numbers with mean zero is a white noise.

### How to check if a given series is stationary or not?¶

#### Visualizations¶

The most basic methods for stationarity detection rely on plotting the data, or functions of it, and determining visually whether they present some known property of stationary (or non-stationary) data. Looking at Autocorrelation Function (ACF) plots. Autocorrelation is the correlation of a signal with a delayed copy — or a lag — of itself as a function of the delay. When plotting the value of the ACF for increasing lags (a plot called a correlogram), the values tend to degrade to zero quickly for stationary time series, while for non-stationary data the degradation will happen more slowly

#### Looking at the data¶

Trying to determine whether a time series was generated by a stationary process just by looking at its plot is a dubious venture. However, there are some basic properties of non-stationary data that we can look for.

#### Parametric tests¶

Another, more rigorous approach, to detecting stationarity in time series data is using statistical tests developed to detect specific types of stationarity, namely those brought about by simple parametric models of the generating stochastic process.

https://www.kdnuggets.com/2019/08/stationarity-time-series-data.html

##### Unit root tests¶

###### The Dickey-Fuller Test¶

The Dickey-Fuller test was the first statistical test developed to test the null hypothesis that a unit root is present in an autoregressive model of a given time series and that the process is thus not stationary. The original test treats the case of a simple lag-1 AR model.

###### The KPSS Test¶

Another prominent test for the presence of a unit root is the KPSS test. [Kwiatkowski et al., 1992] Conversely to the Dickey-Fuller family of tests, the null hypothesis assumes stationarity around a mean or a linear trend, while the alternative is the presence of a unit root.

##### Variance ratio tests¶

These are not usually used as unit root tests, and are instead used for testing whether a financial return series is a pure random walk versus having some predictability. The variance ratio compares the variance of a 1-period return to that of a multi-period return. The comparison length has to be set when initializing the test. This example compares 1-day to 7-day returns, and the null that the series is a pure random walk is rejected. Positive values indicate some negative autocorrelation (momentum).

### How to make a series stationary?¶

Lets understand what is making a TS non-stationary. There are 2 major reasons behind non-stationaruty of a TS:

- Trend – varying mean over time. For eg, average growing over time.
- Seasonality – variations at specific time-frames. eg people might have a tendency to buy cars in a particular month because of pay increment or festivals.

The underlying principle is to model or estimate the trend and seasonality in the series and remove those from the series to get a stationary series. Then statistical forecasting techniques can be implemented on this series. The final step would be to convert the forecasted values into the original scale by applying trend and seasonality constraints back.

### How to treat missing values in a time series?¶

Sometimes, your time series will have missing dates/times. That means, the data was not captured or was not available for those periods. It could so happen the measurement was zero on those days, in which case, case you may fill up those periods with zero.

Secondly, when it comes to time series, you should typically NOT replace missing values with the mean of the series, especially if the series is not stationary. What you could do instead for a quick and dirty workaround is to forward-fill the previous value.

However, depending on the nature of the series, you want to try out multiple approaches before concluding. Some effective alternatives to imputation are:

- Backward Fill
- Linear Interpolation
- Quadratic interpolation
- Mean of nearest neighbors
- Mean of seasonal couterparts

In [12]:

def test_stationary(timeseries): f, (ax) = plt.subplots(1, figsize = (12, 6)) #Determing rolling statistics rolmean = timeseries.rolling(15).mean() rolstd = timeseries.rolling(15).std() #Plot rolling statistics: orig = plt.plot(timeseries, color='blue',label='Original') mean = plt.plot(rolmean, color='red', label='Rolling Mean') std = plt.plot(rolstd, color='black', label = 'Rolling Std') plt.legend(loc='best') plt.title('Rolling Mean & Standard Deviation') plt.show(block=False) #Perform Dickey-Fuller test: print('Results of Dickey-Fuller Test:') dftest = adfuller(timeseries, autolag='AIC') dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used']) for key,value in dftest[4].items(): dfoutput['Critical Value (%s)'%key] = value print(dfoutput) # KPSS test kpss = KPSS(timeseries) print(kpss.summary().as_text()) # Changing the trend print("KPSS change trend") kpss.trend = 'ct' print(kpss.summary().as_text()) # The Zivot-Andrews test allows the possibility of a single structural break in the series. za = ZivotAndrews(timeseries) print(za.summary().as_text()) # Variance Ratio Testing vr = VarianceRatio(timeseries, 7) print(vr.summary().as_text())

In [13]:

test_stationary(df['open'])

Results of Dickey-Fuller Test: Test Statistic -1.277178 p-value 0.639548 #Lags Used 21.000000 Number of Observations Used 1071.000000 Critical Value (1%) -3.436470 Critical Value (5%) -2.864242 Critical Value (10%) -2.568209 dtype: float64 KPSS Stationarity Test Results ===================================== Test Statistic 4.770 P-value 0.000 Lags 20 ------------------------------------- Trend: Constant Critical Values: 0.74 (1%), 0.46 (5%), 0.35 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. KPSS change trend KPSS Stationarity Test Results ===================================== Test Statistic 0.782 P-value 0.000 Lags 20 ------------------------------------- Trend: Constant and Linear Time Trend Critical Values: 0.22 (1%), 0.15 (5%), 0.12 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. Zivot-Andrews Results ===================================== Test Statistic -5.431 P-value 0.005 Lags 20 ------------------------------------- Trend: Constant Critical Values: -5.28 (1%), -4.81 (5%), -4.57 (10%) Null Hypothesis: The process contains a unit root with a single structural break. Alternative Hypothesis: The process is trend and break stationary. Variance-Ratio Test Results ===================================== Test Statistic 0.295 P-value 0.768 Lags 7 ------------------------------------- Computed with overlapping blocks (de-biased)

### Estimating & Eliminating Trend¶

It is easy to see a forward trend in the data. But its not very intuitive in presence of noise. So we can use some techniques to estimate or model this trend and then remove it from the series. There can be many ways of doing it and some of most commonly used are:

- Aggregation – taking average for a time period like monthly/weekly averages
- Smoothing – taking rolling averages
- Polynomial Fitting – fit a regression model

One of the first tricks to reduce trend can be transformation. For example, in this case we can clearly see that the there is a significant positive trend.

#### Log¶

So we can apply transformation which penalize higher values more than smaller values. These can be taking a log, square root, cube root, etc. Lets take a log transform here for simplicity: In [14]:

f, (ax1) = plt.subplots(1, figsize = (12, 8)) ts_log = np.log(df['open']) plt.plot(ts_log) plt.show()

In [15]:

test_stationary(ts_log)

Results of Dickey-Fuller Test: Test Statistic -1.810821 p-value 0.375103 #Lags Used 17.000000 Number of Observations Used 1075.000000 Critical Value (1%) -3.436448 Critical Value (5%) -2.864232 Critical Value (10%) -2.568204 dtype: float64 KPSS Stationarity Test Results ===================================== Test Statistic 4.718 P-value 0.000 Lags 20 ------------------------------------- Trend: Constant Critical Values: 0.74 (1%), 0.46 (5%), 0.35 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. KPSS change trend KPSS Stationarity Test Results ===================================== Test Statistic 1.043 P-value 0.000 Lags 20 ------------------------------------- Trend: Constant and Linear Time Trend Critical Values: 0.22 (1%), 0.15 (5%), 0.12 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. Zivot-Andrews Results ===================================== Test Statistic -4.686 P-value 0.073 Lags 17 ------------------------------------- Trend: Constant Critical Values: -5.28 (1%), -4.81 (5%), -4.57 (10%) Null Hypothesis: The process contains a unit root with a single structural break. Alternative Hypothesis: The process is trend and break stationary. Variance-Ratio Test Results ===================================== Test Statistic 0.254 P-value 0.799 Lags 7 ------------------------------------- Computed with overlapping blocks (de-biased)

We can see that the mean and std variations have large variations with time. Also, the Dickey-Fuller test statistic is not less than the 1% critical value, thus the TS is stationary with 99% confidence.

### Moving average¶

In this approach, we take average of ‘k’ consecutive values depending on the frequency of time series. Here we can take the average over the past 7 days, i.e. last 7 values. Pandas has specific functions defined for determining rolling statistics. In [16]:

f, (ax1) = plt.subplots(1, figsize = (12, 8)) moving_avg = ts_log.rolling(7).mean() plt.plot(ts_log) plt.plot(moving_avg, color='red')

Out[16]:

[<matplotlib.lines.Line2D at 0x2713e0dca20>]

The red line shows the rolling mean. Lets subtract this from the original series. Note that since we are taking average of last 7 values, rolling mean is not defined for first 6 values. This can be observed as: In [17]:

ts_log_moving_avg_diff = ts_log - moving_avg ts_log_moving_avg_diff.head(8)

Out[17]:

index 2016-01-04 NaN 2016-01-05 NaN 2016-01-06 NaN 2016-01-07 NaN 2016-01-08 NaN 2016-01-11 NaN 2016-01-12 -0.030009 2016-01-13 -0.005996 Name: open, dtype: float64

Notice the first 6 being Nan. Lets drop these NaN values and check the plots to test stationarity. In [18]:

ts_log_moving_avg_diff.dropna(inplace=True) test_stationary(ts_log_moving_avg_diff)

Results of Dickey-Fuller Test: Test Statistic -7.665761e+00 p-value 1.642882e-11 #Lags Used 1.900000e+01 Number of Observations Used 1.067000e+03 Critical Value (1%) -3.436493e+00 Critical Value (5%) -2.864253e+00 Critical Value (10%) -2.568214e+00 dtype: float64 KPSS Stationarity Test Results ===================================== Test Statistic 0.129 P-value 0.462 Lags 15 ------------------------------------- Trend: Constant Critical Values: 0.74 (1%), 0.46 (5%), 0.35 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. KPSS change trend KPSS Stationarity Test Results ===================================== Test Statistic 0.029 P-value 0.880 Lags 15 ------------------------------------- Trend: Constant and Linear Time Trend Critical Values: 0.22 (1%), 0.15 (5%), 0.12 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. Zivot-Andrews Results ===================================== Test Statistic -7.930 P-value 0.000 Lags 19 ------------------------------------- Trend: Constant Critical Values: -5.28 (1%), -4.81 (5%), -4.57 (10%) Null Hypothesis: The process contains a unit root with a single structural break. Alternative Hypothesis: The process is trend and break stationary. Variance-Ratio Test Results ===================================== Test Statistic -1.691 P-value 0.091 Lags 7 ------------------------------------- Computed with overlapping blocks (de-biased)

We can see that the mean and std variations have small variations with time. Also, the Dickey-Fuller test statistic is less than the 10% critical value, thus the TS is stationary with 90% confidence. We can also take second or third order differences which might get even better results in certain applications.

### Exponentially-weighted moving average¶

In [19]:

f, (ax1) = plt.subplots(1, figsize = (12, 6)) expwighted_avg = ts_log.ewm(halflife=7).mean() plt.plot(ts_log) plt.plot(expwighted_avg, color='red')

Out[19]:

[<matplotlib.lines.Line2D at 0x2713efbaac8>]

Note that here the parameter ‘halflife’ is used to define the amount of exponential decay. This is just an assumption here and would depend largely on the business domain. Other parameters like span and center of mass can also be used to define decay which are discussed in the link shared above. Now, let’s remove this from series and check stationarity: In [20]:

ts_log_ewma_diff = ts_log - expwighted_avg test_stationary(ts_log_ewma_diff)

Results of Dickey-Fuller Test: Test Statistic -6.303045e+00 p-value 3.375343e-08 #Lags Used 1.900000e+01 Number of Observations Used 1.073000e+03 Critical Value (1%) -3.436459e+00 Critical Value (5%) -2.864237e+00 Critical Value (10%) -2.568206e+00 dtype: float64 KPSS Stationarity Test Results ===================================== Test Statistic 0.245 P-value 0.195 Lags 18 ------------------------------------- Trend: Constant Critical Values: 0.74 (1%), 0.46 (5%), 0.35 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. KPSS change trend KPSS Stationarity Test Results ===================================== Test Statistic 0.044 P-value 0.664 Lags 18 ------------------------------------- Trend: Constant and Linear Time Trend Critical Values: 0.22 (1%), 0.15 (5%), 0.12 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. Zivot-Andrews Results ===================================== Test Statistic -6.578 P-value 0.000 Lags 19 ------------------------------------- Trend: Constant Critical Values: -5.28 (1%), -4.81 (5%), -4.57 (10%) Null Hypothesis: The process contains a unit root with a single structural break. Alternative Hypothesis: The process is trend and break stationary. Variance-Ratio Test Results ===================================== Test Statistic -0.977 P-value 0.329 Lags 7 ------------------------------------- Computed with overlapping blocks (de-biased)

We can see that the mean and std variations have small variations with time. Also, the Dickey-Fuller test statistic is less than the 10% critical value, thus the TS is stationary with 90% confidence. We can also take second or third order differences which might get even better results in certain applications.

## Why and How to smoothen a time series?¶

Smoothening of a time series may be useful in:

Reducing the effect of noise in a signal get a fair approximation of the noise-filtered series. The smoothed version of series can be used as a feature to explain the original series itself. Visualize the underlying trend better So how to smoothen a series? Let’s discuss the following methods:

Take a moving average Do a LOESS smoothing (Localized Regression) Do a LOWESS smoothing (Locally Weighted Regression) Moving average is nothing but the average of a rolling window of defined width. But you must choose the window-width wisely, because, large window-size will over-smooth the series. For example, a window-size equal to the seasonal duration (ex: 12 for a month-wise series), will effectively nullify the seasonal effect.

LOESS, short for ‘LOcalized regrESSion’ fits multiple regressions in the local neighborhood of each point. It is implemented in the statsmodels package, where you can control the degree of smoothing using frac argument which specifies the percentage of data points nearby that should be considered to fit a regression model.

https://www.machinelearningplus.com/time-series/time-series-analysis-python/ In [21]:

from statsmodels.nonparametric.smoothers_lowess import lowess plt.rcParams.update({'xtick.bottom' : False, 'axes.titlepad':5}) # Import df_orig = df['open'] # 1. Moving Average df_ma = df_orig.rolling(7, center=True, closed='both').mean() # 2. Loess Smoothing (5% and 15%) df_loess_5 = pd.DataFrame(lowess(df_orig, np.arange(len(df_orig)), frac=0.05)[:, 1], index=df_orig.index, columns=['value']) df_loess_15 = pd.DataFrame(lowess(df_orig, np.arange(len(df_orig)), frac=0.15)[:, 1], index=df_orig.index, columns=['value']) # Plot fig, axes = plt.subplots(4,1, figsize=(7, 7), sharex=True, dpi=120) df_orig.plot(ax=axes[0], color='k', title='Original Series') df_loess_5.plot(ax=axes[1], title='Loess Smoothed 5%') df_loess_15.plot(ax=axes[2], title='Loess Smoothed 15%') df_ma.plot(ax=axes[3], title='Moving Average (3)') fig.suptitle('How to Smoothen a Time Series', y=0.95, fontsize=14) plt.show()

## Eliminating Trend and Seasonality¶

The simple trend reduction techniques discussed before don’t work in all cases, particularly the ones with high seasonality. Lets discuss two ways of removing trend and seasonality:

- Differencing – taking the differece with a particular time lag
- Decomposition – modeling both trend and seasonality and removing them from the model.

#### Differencing¶

One of the most common methods of dealing with both trend and seasonality is differencing. In this technique, we take the difference of the observation at a particular instant with that at the previous instant. This mostly works well in improving stationarity. First order differencing can be done in Pandas as: In [22]:

f, (ax1) = plt.subplots(1, figsize = (12, 6)) ts_log_diff = ts_log - ts_log.shift(7) plt.plot(ts_log_diff)

Out[22]:

[<matplotlib.lines.Line2D at 0x2713f01b908>]

In [23]:

ts_log_diff

Out[23]:

index 2016-01-04 NaN 2016-01-05 NaN 2016-01-06 NaN 2016-01-07 NaN 2016-01-08 NaN ... 2020-04-30 0.041673 2020-05-01 0.031025 2020-05-04 0.019271 2020-05-05 0.009251 2020-05-06 0.006425 Name: open, Length: 1093, dtype: float64

In [24]:

ts_log_diff.dropna(inplace=True) test_stationary(ts_log_diff)

Results of Dickey-Fuller Test: Test Statistic -6.436710e+00 p-value 1.645978e-08 #Lags Used 2.200000e+01 Number of Observations Used 1.063000e+03 Critical Value (1%) -3.436517e+00 Critical Value (5%) -2.864263e+00 Critical Value (10%) -2.568220e+00 dtype: float64 KPSS Stationarity Test Results ===================================== Test Statistic 0.130 P-value 0.456 Lags 17 ------------------------------------- Trend: Constant Critical Values: 0.74 (1%), 0.46 (5%), 0.35 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. KPSS change trend KPSS Stationarity Test Results ===================================== Test Statistic 0.031 P-value 0.854 Lags 17 ------------------------------------- Trend: Constant and Linear Time Trend Critical Values: 0.22 (1%), 0.15 (5%), 0.12 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. Zivot-Andrews Results ===================================== Test Statistic -6.695 P-value 0.000 Lags 22 ------------------------------------- Trend: Constant Critical Values: -5.28 (1%), -4.81 (5%), -4.57 (10%) Null Hypothesis: The process contains a unit root with a single structural break. Alternative Hypothesis: The process is trend and break stationary. Variance-Ratio Test Results ===================================== Test Statistic 1.289 P-value 0.197 Lags 7 ------------------------------------- Computed with overlapping blocks (de-biased)

#### This does not appear to have reduced trend considerably.¶

The Dickey-Fuller test statistic is not significantly lower than the 1% critical value. So this TS is not very close to stationary. We can try advanced decomposition techniques as well which can generate better results. Also, you should note that converting the residuals into original values for future data in not very intuitive in this case.

### Decomposition¶

We can model the residuals of the the logs. Lets check stationarity of residuals: In [25]:

decomposition = seasonal_decompose(ts_log_diff, model='additive', freq = 52) ts_log_decompose = decomposition.resid ts_log_decompose.dropna(inplace=True) test_stationary(ts_log_decompose)

Results of Dickey-Fuller Test: Test Statistic -9.404658e+00 p-value 6.056794e-16 #Lags Used 2.200000e+01 Number of Observations Used 1.011000e+03 Critical Value (1%) -3.436835e+00 Critical Value (5%) -2.864403e+00 Critical Value (10%) -2.568294e+00 dtype: float64 KPSS Stationarity Test Results ===================================== Test Statistic 0.093 P-value 0.620 Lags 16 ------------------------------------- Trend: Constant Critical Values: 0.74 (1%), 0.46 (5%), 0.35 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. KPSS change trend KPSS Stationarity Test Results ===================================== Test Statistic 0.035 P-value 0.806 Lags 16 ------------------------------------- Trend: Constant and Linear Time Trend Critical Values: 0.22 (1%), 0.15 (5%), 0.12 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. Zivot-Andrews Results ===================================== Test Statistic -9.469 P-value 0.000 Lags 22 ------------------------------------- Trend: Constant Critical Values: -5.28 (1%), -4.81 (5%), -4.57 (10%) Null Hypothesis: The process contains a unit root with a single structural break. Alternative Hypothesis: The process is trend and break stationary. Variance-Ratio Test Results ===================================== Test Statistic 0.789 P-value 0.430 Lags 7 ------------------------------------- Computed with overlapping blocks (de-biased)

### Let us see the performance of ts_log_diff on modelling.¶

### How to estimate the forecastability of a time series?¶

The more regular and repeatable patterns a time series has, the easier it is to forecast. The ‘Approximate Entropy’ can be used to quantify the regularity and unpredictability of fluctuations in a time series. The higher the approximate entropy, the more difficult it is to forecast it.

Another better alternate is the ‘Sample Entropy’.

Sample Entropy is similar to approximate entropy but is more consistent in estimating the complexity even for smaller time series. For example, a random time series with fewer data points can have a lower ‘approximate entropy’ than a more ‘regular’ time series, whereas, a longer random time series will have a higher ‘approximate entropy’. Sample Entropy handles this problem nicely. In [26]:

# https://en.wikipedia.org/wiki/Approximate_entropy def ApEn(U, m, r): """Compute Aproximate entropy""" def _maxdist(x_i, x_j): return max([abs(ua - va) for ua, va in zip(x_i, x_j)]) def _phi(m): x = [[U[j] for j in range(i, i + m - 1 + 1)] for i in range(N - m + 1)] C = [len([1 for x_j in x if _maxdist(x_i, x_j) <= r]) / (N - m + 1.0) for x_i in x] return (N - m + 1.0)**(-1) * sum(np.log(C)) N = len(U) return abs(_phi(m+1) - _phi(m)) print(ApEn(ts_log_diff, m=2, r=0.2*np.std(ts_log_diff)))

1.1685669292469725

In [27]:

# https://en.wikipedia.org/wiki/Sample_entropy def SampEn(U, m, r): """Compute Sample entropy""" def _maxdist(x_i, x_j): return max([abs(ua - va) for ua, va in zip(x_i, x_j)]) def _phi(m): x = [[U[j] for j in range(i, i + m - 1 + 1)] for i in range(N - m + 1)] C = [len([1 for j in range(len(x)) if i != j and _maxdist(x[i], x[j]) <= r]) for i in range(len(x))] return sum(C) N = len(U) return -np.log(_phi(m+1) / _phi(m)) print(SampEn(ts_log_diff, m=2, r=0.2*np.std(ts_log_diff)))

1.0896107625328675

### Checking Stationarity (ACF and PACF Plots)¶

Another common method of checking to see if data is stationary is to plot its autocorrelation function.

- Autocorrelation Function (ACF): It is a measure of the correlation between the the TS with a lagged version of itself. For instance at lag 5, ACF would compare series at time instant ‘t1’…’t2’ with series at instant ‘t1-5’…’t2-5’ (t1-5 and t2 being end points).
- Partial Autocorrelation Function (PACF): This measures the correlation between the TS with a lagged version of itself but after eliminating the variations already explained by the intervening comparisons. Eg at lag 5, it will check the correlation but remove the effects already explained by lags 1 to 4.

The autocorrelation involves “sliding” or “shifting” a function and multiplying it with its unshifted self. This allows one to measure the overlap of the function with itself at different points in time. This process ends up being useful for discovering periodicity in ones data. For example, a sine wave repeats itself every 2\pi2π, so one would see peaks in the autocorrelation function every 2\pi2π.

We can use statsmodels to plot our autocorrelation function. We can use the autocorrelation function to quantify this. Autocorrelations always start at 1 (a function perfectly overlaps with itself), but we would like to see that it drops down close to zero. These plots show that it certaintly does not!

Autocorrelation is a problem in regular regressions like above, but we’ll use it to our advantage when we setup an ARIMA model below. The basic idea is pretty sensible: if your regression residuals have a clear pattern, then there’s clearly some structure in the data that you aren’t taking advantage of. If a positive residual today means you’ll likely have a positive residual tomorrow, why not incorporate that information into your forecast, and lower your forecasted value for tomorrow? That’s pretty much what ARIMA does. In [28]:

#ACF and PACF plots: from statsmodels.tsa.stattools import acf, pacf def viz_stationary(timeseries, n_lags): lag_acf = acf(timeseries, nlags=n_lags) lag_pacf = pacf(timeseries, nlags=n_lags, method='ols') f, (ax1) = plt.subplots(1, figsize = (10, 5)) #Plot ACF: plt.subplot(121) plt.plot(lag_acf) plt.axhline(y=0,linestyle='--',color='gray') plt.axhline(y=-1.96/np.sqrt(len(timeseries)),linestyle='--',color='gray') plt.axhline(y=1.96/np.sqrt(len(timeseries)),linestyle='--',color='gray') plt.title('Autocorrelation Function') #Plot PACF: plt.subplot(122) plt.plot(lag_pacf) plt.axhline(y=0,linestyle='--',color='gray') plt.axhline(y=-1.96/np.sqrt(len(timeseries)),linestyle='--',color='gray') plt.axhline(y=1.96/np.sqrt(len(timeseries)),linestyle='--',color='gray') plt.title('Partial Autocorrelation Function') plt.tight_layout() viz_stationary(ts_log_diff,7)

In [29]:

# from pandas.plotting import autocorrelation_plot # f, (ax1) = plt.subplots(1, figsize = (8, 6)) # autocorrelation_plot(ts_log_diff.tolist())

## Forecasting a Time Series¶

### What is an ARIMA model?¶

ARIMA stands for Auto-Regressive Integrated Moving Averages. The ARIMA forecasting for a stationary time series is nothing but a linear (like a linear regression) equation. The predictors depend on the parameters (p,d,q) of the ARIMA model:

- Number of AR (Auto-Regressive) terms (p): AR terms are just lags of dependent variable. For instance if p is 5, the predictors for x(t) will be x(t-1)….x(t-5).
- Number of MA (Moving Average) terms (q): MA terms are lagged forecast errors in prediction equation. For instance if q is 5, the predictors for x(t) will be e(t-1)….e(t-5) where e(i) is the difference between the moving average at ith instant and actual value.
- Number of Differences (d): These are the number of nonseasonal differences, i.e. in this case we took the first order difference. So either we can pass that variable and put d=0 or pass the original variable and put d=1. Both will generate same results.

We determined the value of ‘p’ and ‘q’ using the plots above.

In the plot, the two dotted lines on either sides of 0 are the confidence interevals. These can be used to determine the ‘p’ and ‘q’ values as:

- p – The lag value where the PACF chart crosses the upper confidence interval for the first time. If you notice closely, in this case p=2.
- q – The lag value where the ACF chart crosses the upper confidence interval for the first time. If you notice closely, in this case q=5.
- D, we need to look which lagged version differencing made the series stationary.if X — X.shift(1) makes your X series stationary, D =1.

Now, lets make 3 different ARIMA models considering individual as well as combined effects. I will also print the RSS for each. Please note that here RSS is for the values of residuals and not actual series.

We need to load the ARIMA model first:

- P = Using AutoCorrelation plot for AR
- D = For Integrated term in AR-I-MA
- Q = Using PartialAutoCorrelation plot for MA

### AR Model¶

In [30]:

model = AR(ts_log_diff) results_AR = model.fit(disp=-1) f, (ax1) = plt.subplots(1, figsize = (12, 6)) plt.plot(ts_log_diff[len(ts_log_diff) - len(results_AR.fittedvalues):]) plt.plot(results_AR.fittedvalues, color='red') plt.title('RMSE: %.4f'% np.sqrt(sum((results_AR.fittedvalues- ts_log_diff[len(ts_log_diff) - len(results_AR.fittedvalues):])**2))) plt.show()

### Autoregressive Moving-Average Processes (ARMA) Model¶

In [31]:

model = ARMA(ts_log_diff, order=(2, 1, 5)) results_MA = model.fit(disp=-1) f, (ax1) = plt.subplots(1, figsize = (12, 6)) plt.plot(ts_log_diff) plt.plot(results_MA.fittedvalues, color='red') plt.title('RMSE: %.4f'% np.sqrt(sum((results_MA.fittedvalues-ts_log_diff)**2))) plt.show()

### Combined Model¶

In [32]:

model = ARIMA(ts_log_diff, order=(2, 1, 5)) results_ARIMA = model.fit(disp=-7) f, (ax1) = plt.subplots(1, figsize = (12, 6)) plt.plot(ts_log_diff[len(ts_log_diff) - len(results_ARIMA.fittedvalues):]) plt.plot(results_ARIMA.fittedvalues, color='red') plt.title('RMSE: %.4f'% np.sqrt(sum((results_ARIMA.fittedvalues- ts_log_diff[len(ts_log_diff) - len(results_ARIMA.fittedvalues):])**2))) plt.show()

Here we can see that the ARIMA is not the best model while MA and AR models have almost the same RSS but AR is significantly better.

## Taking it back to original scale¶

Since the AR model gave best result, lets scale it back to the original values and see how well it performs there. First step would be to store the predicted results as a separate series and observe it.

Once you are done with forecasting, don’t forget to trace back all transformation you made to your original series(most common mistake). Additionally, in the same reverse sequence(follow LIFO, Last applied transformation reverted first). Do create a copy of your series. It will help you out in recovering things back. For example, if you applied log() first and then differencing, first add what has been subtracted and than exp() over that. In [33]:

predictions_AR_diff = pd.Series(results_AR.fittedvalues, copy=True) print(predictions_AR_diff.head())

index 2016-02-16 -0.024557 2016-02-17 -0.000758 2016-02-18 0.037416 2016-02-19 0.075096 2016-02-22 0.067324 dtype: float64

Notice that these start from ‘2016-02-16 ’ and not the first day.

This is because we took a lag and first elements doesn’t have anything before it to subtract from. The way to convert the differencing to log scale is to add these differences consecutively to the base number. In [34]:

normal_values = np.exp(predictions_AR_diff) + np.exp(ts_log.shift(7))

### Fill in the null values with the true values¶

In [35]:

predictions_values = normal_values.combine_first(np.exp(ts_log))

In [36]:

predictions_values.head()

Out[36]:

index 2016-01-04 116.91 2016-01-05 116.72 2016-01-06 115.78 2016-01-07 113.63 2016-01-08 112.35 dtype: float64

In [37]:

#predictions_AR_log = np.exp(predictions_AR_log) f, (ax1) = plt.subplots(1, figsize = (12, 6)) plt.plot(df['open'], label = 'true') plt.plot(predictions_values, label = 'predicted') plt.title('RMSE: %.4f'% np.sqrt(sum((predictions_values-df['open'])**2)/len(df['open']))) plt.legend(loc="upper left") plt.show()

Classical & Statistical Time Series Modelling of United Health Group’s Stock Price

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Time series is different from a regular regression problem because it is time dependent. The basic assumption of a linear regression that the observations are independent doesn’t hold in this case. Along with an increasing or decreasing trend, most time series have some form of seasonality trends, i.e. variations specific to a particular time frame. Analysis of a time series is important because it is the preparatory step before you develop a forecast of the series. It involves understanding various aspects about the inherent nature of the series so that you are better informed to create meaningful and accurate forecasts.

In this blog post we’ll examine some common techniques used in time series analysis by applying them to a data set containing daily opening values for the United Health Group from 2016 up to present day.

UnitedHealth Group Incorporated is an American for-profit managed health care company based in Minnetonka, Minnesota. It offers health care products and insurance services. It is the largest healthcare company in the world by revenue, with 2019 revenue of $242.2 billion. The company is ranked 6th on the 2019 Fortune 500.

## Model Based Forecasting includes¶

#### Classical & Statistical Learning¶

- Autoregressive integrated moving average (ARIMA)
- Exponential smoothing methods (e.g. Holt-Winters)
- Theta

When the underlying mechanisms are not known or are too complicated, e.g., the stock market, or not fully known, e.g., retail sales, it is usually better to apply a simple statistical model. Popular classical methods that belong to this category include ARIMA (autoregressive integrated moving average), exponential smoothing methods, such as Holt-Winters, and the Theta method.

#### Machine Learning¶

- Recurrent neural networks (RNN)
- Quantile regression forest (QRF)
- Gradient boosting trees (GBM)
- Support vector regression (SVR)
- Gaussian Process regression (GP)

Machine learning models are of a black-box type and are used when interpretability is not a requirement. Both, classical and ML methods are not that different from each other, but distinguished by whether the models are more simple and interpretable or more complex and flexible. In practice. classical statistical algorithms tend to be much quicker and easier-to-use.

## Framework¶

### Step 1: Visualize the Time Series¶

It is essential to analyze the trends prior to building any kind of time series model. The details we are interested in pertains to any kind of trend, seasonality or random behaviour in the series.

### Step 2: Stationarize the Series¶

Once we know the patterns, trends, cycles and seasonality , we can check if the series is stationary or not. Dickey – Fuller is one of the popular test to check the same. If the series is non-stationary, we can use one of the commonly used techniques to make a time series stationary:

- Detrending : Here, we simply remove the trend component from the time series. For instance, the equation of my time series is:

x(t) = (mean + trend * t) + error

We’ll simply remove the part in the parentheses and build model for the rest.

- Differencing : This is the commonly used technique to remove non-stationarity. Here we try to model the differences of the terms and not the actual term. For instance,

x(t) – x(t-1) = ARMA (p , q)

This differencing is called as the Integration part in AR(I)MA. Now, we have three parameters

p : AR (AutoRegressive = the model takes advantage of the connection between a predefined number of lagged observations and the current one.)

d : I (Integrated = differencing between raw observations (eg. subtracting observations at different time steps).)

q : MA (Moving Average = the model takes advantage of the relationship between the residual error and the observations.)

- Seasonality : Seasonality can easily be incorporated in the ARIMA model directly.

### Step 3: Find Optimal Parameters¶

The parameters p,d,q can be found using ACF and PACF plots. An addition to this approach is can be, if both ACF and PACF decreases gradually, it indicates that we need to make the time series stationary and introduce a value to “d”.

### Step 4: Build ARIMA Model¶

With the parameters in hand, we build ARIMA model. The value found in the previous section might be an approximate estimate and we need to explore more (p,d,q) combinations. The one with the lowest BIC and AIC should be our choice. We can also try some models with a seasonal component. Just in case, we notice any seasonality in ACF/PACF plots.

### Step 5: Make Predictions¶

Once we have the final ARIMA model, we are ready to make predictions on the future time points. We can also visualize the trends to cross validate if the model works fine.

### References for this post:¶

- https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/
- https://www.analyticsvidhya.com/blog/2015/12/complete-tutorial-time-series-modeling/
- https://medium.com/analytics-vidhya/preprocessing-for-time-series-forecasting-3a331dbfb9c2
- http://www.gregreda.com/2020/02/16/feature-engineering-with-time-gaps/
- https://www.machinelearningplus.com/time-series/time-series-analysis-python/
- https://www.kdnuggets.com/2019/08/stationarity-time-series-data.html
- http://bashtage.github.io/arch/doc/index.html
- https://www.alphavantage.co/documentation/
- https://towardsdatascience.com/stock-market-analysis-using-arima-8731ded2447a

In [38]:

import pandas as pd import numpy as np import seaborn as sns # advanced vizs import matplotlib.pyplot as plt from matplotlib.pyplot import figure from matplotlib.pyplot import suptitle import matplotlib.style as style from IPython.display import display, HTML # statistics from statsmodels.distributions.empirical_distribution import ECDF # time series analysis from statsmodels.tsa.seasonal import seasonal_decompose from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from statsmodels.tsa.stattools import adfuller from arch.unitroot import KPSS, ZivotAndrews, VarianceRatio from statsmodels.tsa.arima_model import AR, ARMA, ARIMA import warnings %matplotlib inline %config InlineBackend.figure_format = 'png' #set 'png' here when working on notebook plt.style.use('bmh') warnings.filterwarnings('ignore') pd.set_option('display.width',100, 'display.max_rows',20, 'display.max_columns',9,'max_colwidth',100)

## Download Data¶

In [2]:

from datetime import datetime import pandas_datareader.data as web import configparser settings = configparser.ConfigParser() settings.read('../data/processed/config.ini') api_key = settings.get('AlphaVantage', 'api_key') start = datetime(2016, 1, 1) end = datetime.now() f = web.DataReader("UNH", "av-daily", start=start, end=end, api_key= api_key)

In [3]:

f.head()

Out[3]:

open | high | low | close | volume | |
---|---|---|---|---|---|

2016-01-04 | 116.91 | 116.91 | 114.525 | 116.46 | 4990855 |

2016-01-05 | 116.72 | 117.89 | 116.210 | 116.68 | 2816727 |

2016-01-06 | 115.78 | 116.65 | 114.500 | 115.49 | 2677303 |

2016-01-07 | 113.63 | 114.09 | 111.420 | 112.09 | 5276916 |

2016-01-08 | 112.35 | 113.00 | 109.760 | 110.16 | 4422365 |

## Select the Opening Price¶

In [4]:

df = f['open'].to_frame().reset_index() df.columns =['index','open'] df['index'] = pd.to_datetime(df['index']) df.set_index("index", inplace=True)

## Plot¶

In [5]:

f, (ax1) = plt.subplots(1, figsize = (12, 6)) # preparation: input should be float type plt.plot(df) plt.show()

### Resample¶

In [6]:

f, (ax1) = plt.subplots(1, figsize = (12, 6)) # Weekly Resample df1 = df['open'].resample('W').sum().reset_index() plt.plot(df1['index'],df1['open']) plt.show()

### Patterns in a time series¶

Any time series may be split into the following components:

Base Level + Trend + Seasonality + Error

A trend is observed when there is an increasing or decreasing slope observed in the time series. Whereas seasonality is observed when there is a distinct repeated pattern observed between regular intervals due to seasonal factors. It could be because of the month of the year. It happens when the rise and fall pattern in the series does not happen in fixed calendar-based intervals. However, It is not mandatory that all time series must have a trend and/or seasonality. A time series may not have a distinct trend but have a seasonality. The opposite can also be true. If the patterns are not of fixed calendar based frequencies, then it is cyclic. Because, unlike the seasonality, cyclic effects are typically influenced by the business and other socio-economic factors. Furthermore, care should be taken to not confuse ‘cyclic’ effect with ‘seasonal’ effect.

#### How to diffentiate between a ‘cyclic’ vs ‘seasonal’?¶

(Additive and multiplicative time series)

Depending on the nature of the trend and seasonality, a time series can be modeled as an additive or multiplicative, wherein, each observation in the series can be expressed as either a sum or a product of the components:

Additive time series: Value = Base Level + Trend + Seasonality + Error

Multiplicative Time Series: Value = Base Level x Trend x Seasonality x Error In [7]:

def pattern_series(timeseries, model, freq): f, (ax1) = plt.subplots(1, figsize = (12, 9)) decomposition = seasonal_decompose(timeseries, model=model, freq = freq) trend = decomposition.trend seasonal = decomposition.seasonal residual = decomposition.resid plt.suptitle(model) plt.subplot(411) plt.plot(timeseries, label='Original') plt.legend(loc='best') plt.subplot(412) plt.plot(trend, label='Trend') plt.legend(loc='best') plt.subplot(413) plt.plot(seasonal,label='Seasonality') plt.legend(loc='best') plt.subplot(414) plt.plot(residual, label='Residuals') plt.legend(loc='best') plt.tight_layout() plt.show()

### Decomposing¶

In this approach, both trend and seasonality are modeled separately and the remaining part of the series is returned. In [8]:

### Multiplicative pattern_series(df['open'], 'multiplicative', 52)

In [9]:

### Additive pattern_series(df['open'],'additive', 52)

Here we can see that the trend, seasonality are separated out from data. If we look at the residuals of the multiplicative decomposition closely, it has no pattern left over. However, the addtive decomposition looks quite random which is good. Additive decomposition should be preferred for this particular series.

Note: Setting extrapolate_trend=’freq’ takes care of any missing values in the trend and residuals at the beginning of the series.

The numerical output of the trend, seasonal and residual components are stored in the decomposition output itself. We can extract them and put it in a dataframe. In [10]:

# Extract the Components ---- # Actual Values = Sum of (Seasonal + Trend + Resid) decomposition = seasonal_decompose(df['open'], model='additive', freq = 52) df_reconstructed = pd.concat([decomposition.seasonal, decomposition.trend, decomposition.resid, decomposition.observed], axis=1) df_reconstructed.columns = ['seas', 'trend', 'resid', 'actual_values'] df_reconstructed.head()

Out[10]:

seas | trend | resid | actual_values | |
---|---|---|---|---|

index | ||||

2016-01-04 | 1.591094 | NaN | NaN | 116.91 |

2016-01-05 | 1.208426 | NaN | NaN | 116.72 |

2016-01-06 | 0.394594 | NaN | NaN | 115.78 |

2016-01-07 | 1.002825 | NaN | NaN | 113.63 |

2016-01-08 | 0.330801 | NaN | NaN | 112.35 |

## Lag Plots¶

A Lag plot is a scatter plot of a time series against a lag of itself. It is normally used to check for autocorrelation. If there is any pattern existing in the series like the one we see below, the series is autocorrelated. If there is no such pattern, the series is likely to be random white noise. In [11]:

# Lag Plot f, (ax1) = plt.subplots(1, figsize = (9, 6)) pd.plotting.lag_plot(df['open'], lag = 7)

Out[11]:

<matplotlib.axes._subplots.AxesSubplot at 0x2713cbc4898>

## Stationarity¶

We cannot build a time series model unless our time series is stationary. A stationary series is one where the values of the series is not a function of time. It’s statistical properties such as mean, variance remain constant over time. Autocorrelation of the series is nothing but the correlation of the series with its previous values.

### Why make a non-stationary series stationary before forecasting?¶

An important reason is, autoregressive forecasting models are essentially linear regression models that utilize the lag(s) of the series itself as predictors. We know that linear regression works best if the predictors (X variables) are not correlated against each other. So, stationarizing the series solves this problem since it removes any persistent autocorrelation, thereby making the predictors(lags of the series) in the forecasting models nearly independent. Stationarizing the data is a unique aspect of ARIMA models – it helps to determine the parameters of the ARIMA model. If the residuals are stationary after being fed through a linear model, then the Gauss-Markov theorem guarantees us that we have found the best unbiased linear estimator (BLUE) of the data. Another way to think about this is that, if we see that the residuals are not stationary, then there is probably some pattern in the data that we should be able to incorporate into our model such that the residuals become stationary.

It’s also important that the dataset is stationary, otherwise we run the risk of finding spurious correlations. A common example is the relationship between number of TVs per person and life expectancy. It’s not likely that there’s an actual causal relationship there. Rather, there could be a third variable that’s driving both (wealth, say).

### What is the difference between white noise and a stationary series?¶

Like a stationary series, the white noise is also not a function of time, that is its mean and variance does not change over time. But the difference is, the white noise is completely random with a mean of 0. In white noise there is no pattern whatsoever. If we consider the sound signals in an FM radio as a time series, the blank sound you hear between the channels is white noise. Mathematically, a sequence of completely random numbers with mean zero is a white noise.

### How to check if a given series is stationary or not?¶

#### Visualizations¶

The most basic methods for stationarity detection rely on plotting the data, or functions of it, and determining visually whether they present some known property of stationary (or non-stationary) data. Looking at Autocorrelation Function (ACF) plots. Autocorrelation is the correlation of a signal with a delayed copy — or a lag — of itself as a function of the delay. When plotting the value of the ACF for increasing lags (a plot called a correlogram), the values tend to degrade to zero quickly for stationary time series, while for non-stationary data the degradation will happen more slowly

#### Looking at the data¶

Trying to determine whether a time series was generated by a stationary process just by looking at its plot is a dubious venture. However, there are some basic properties of non-stationary data that we can look for.

#### Parametric tests¶

Another, more rigorous approach, to detecting stationarity in time series data is using statistical tests developed to detect specific types of stationarity, namely those brought about by simple parametric models of the generating stochastic process.

##### Unit root tests¶

###### The Dickey-Fuller Test¶

The Dickey-Fuller test was the first statistical test developed to test the null hypothesis that a unit root is present in an autoregressive model of a given time series and that the process is thus not stationary. The original test treats the case of a simple lag-1 AR model.

###### The KPSS Test¶

Another prominent test for the presence of a unit root is the KPSS test. [Kwiatkowski et al., 1992] Conversely to the Dickey-Fuller family of tests, the null hypothesis assumes stationarity around a mean or a linear trend, while the alternative is the presence of a unit root.

##### Variance ratio tests¶

These are not usually used as unit root tests, and are instead used for testing whether a financial return series is a pure random walk versus having some predictability. The variance ratio compares the variance of a 1-period return to that of a multi-period return. The comparison length has to be set when initializing the test. This example compares 1-day to 7-day returns, and the null that the series is a pure random walk is rejected. Positive values indicate some negative autocorrelation (momentum).

### How to make a series stationary?¶

Lets understand what is making a TS non-stationary. There are 2 major reasons behind non-stationaruty of a TS:

- Trend – varying mean over time. For eg, average growing over time.
- Seasonality – variations at specific time-frames. eg people might have a tendency to buy cars in a particular month because of pay increment or festivals.

The underlying principle is to model or estimate the trend and seasonality in the series and remove those from the series to get a stationary series. Then statistical forecasting techniques can be implemented on this series. The final step would be to convert the forecasted values into the original scale by applying trend and seasonality constraints back.

### How to treat missing values in a time series?¶

Sometimes, your time series will have missing dates/times. That means, the data was not captured or was not available for those periods. It could so happen the measurement was zero on those days, in which case, case you may fill up those periods with zero.

Secondly, when it comes to time series, you should typically NOT replace missing values with the mean of the series, especially if the series is not stationary. What you could do instead for a quick and dirty workaround is to forward-fill the previous value.

However, depending on the nature of the series, you want to try out multiple approaches before concluding. Some effective alternatives to imputation are:

- Backward Fill
- Linear Interpolation
- Quadratic interpolation
- Mean of nearest neighbors
- Mean of seasonal couterparts

In [12]:

def test_stationary(timeseries): f, (ax) = plt.subplots(1, figsize = (12, 6)) #Determing rolling statistics rolmean = timeseries.rolling(15).mean() rolstd = timeseries.rolling(15).std() #Plot rolling statistics: orig = plt.plot(timeseries, color='blue',label='Original') mean = plt.plot(rolmean, color='red', label='Rolling Mean') std = plt.plot(rolstd, color='black', label = 'Rolling Std') plt.legend(loc='best') plt.title('Rolling Mean & Standard Deviation') plt.show(block=False) #Perform Dickey-Fuller test: print('Results of Dickey-Fuller Test:') dftest = adfuller(timeseries, autolag='AIC') dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used']) for key,value in dftest[4].items(): dfoutput['Critical Value (%s)'%key] = value print(dfoutput) # KPSS test kpss = KPSS(timeseries) print(kpss.summary().as_text()) # Changing the trend print("KPSS change trend") kpss.trend = 'ct' print(kpss.summary().as_text()) # The Zivot-Andrews test allows the possibility of a single structural break in the series. za = ZivotAndrews(timeseries) print(za.summary().as_text()) # Variance Ratio Testing vr = VarianceRatio(timeseries, 7) print(vr.summary().as_text())

In [13]:

test_stationary(df['open'])

Results of Dickey-Fuller Test: Test Statistic -1.277178 p-value 0.639548 #Lags Used 21.000000 Number of Observations Used 1071.000000 Critical Value (1%) -3.436470 Critical Value (5%) -2.864242 Critical Value (10%) -2.568209 dtype: float64 KPSS Stationarity Test Results ===================================== Test Statistic 4.770 P-value 0.000 Lags 20 ------------------------------------- Trend: Constant Critical Values: 0.74 (1%), 0.46 (5%), 0.35 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. KPSS change trend KPSS Stationarity Test Results ===================================== Test Statistic 0.782 P-value 0.000 Lags 20 ------------------------------------- Trend: Constant and Linear Time Trend Critical Values: 0.22 (1%), 0.15 (5%), 0.12 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. Zivot-Andrews Results ===================================== Test Statistic -5.431 P-value 0.005 Lags 20 ------------------------------------- Trend: Constant Critical Values: -5.28 (1%), -4.81 (5%), -4.57 (10%) Null Hypothesis: The process contains a unit root with a single structural break. Alternative Hypothesis: The process is trend and break stationary. Variance-Ratio Test Results ===================================== Test Statistic 0.295 P-value 0.768 Lags 7 ------------------------------------- Computed with overlapping blocks (de-biased)

### Estimating & Eliminating Trend¶

It is easy to see a forward trend in the data. But its not very intuitive in presence of noise. So we can use some techniques to estimate or model this trend and then remove it from the series. There can be many ways of doing it and some of most commonly used are:

- Aggregation – taking average for a time period like monthly/weekly averages
- Smoothing – taking rolling averages
- Polynomial Fitting – fit a regression model

One of the first tricks to reduce trend can be transformation. For example, in this case we can clearly see that the there is a significant positive trend.

#### Log¶

So we can apply transformation which penalize higher values more than smaller values. These can be taking a log, square root, cube root, etc. Lets take a log transform here for simplicity: In [14]:

f, (ax1) = plt.subplots(1, figsize = (12, 8)) ts_log = np.log(df['open']) plt.plot(ts_log) plt.show()

In [15]:

test_stationary(ts_log)

Results of Dickey-Fuller Test: Test Statistic -1.810821 p-value 0.375103 #Lags Used 17.000000 Number of Observations Used 1075.000000 Critical Value (1%) -3.436448 Critical Value (5%) -2.864232 Critical Value (10%) -2.568204 dtype: float64 KPSS Stationarity Test Results ===================================== Test Statistic 4.718 P-value 0.000 Lags 20 ------------------------------------- Trend: Constant Critical Values: 0.74 (1%), 0.46 (5%), 0.35 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. KPSS change trend KPSS Stationarity Test Results ===================================== Test Statistic 1.043 P-value 0.000 Lags 20 ------------------------------------- Trend: Constant and Linear Time Trend Critical Values: 0.22 (1%), 0.15 (5%), 0.12 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. Zivot-Andrews Results ===================================== Test Statistic -4.686 P-value 0.073 Lags 17 ------------------------------------- Trend: Constant Critical Values: -5.28 (1%), -4.81 (5%), -4.57 (10%) Null Hypothesis: The process contains a unit root with a single structural break. Alternative Hypothesis: The process is trend and break stationary. Variance-Ratio Test Results ===================================== Test Statistic 0.254 P-value 0.799 Lags 7 ------------------------------------- Computed with overlapping blocks (de-biased)

We can see that the mean and std variations have large variations with time. Also, the Dickey-Fuller test statistic is not less than the 1% critical value, thus the TS is stationary with 99% confidence.

### Moving average¶

In this approach, we take average of ‘k’ consecutive values depending on the frequency of time series. Here we can take the average over the past 7 days, i.e. last 7 values. Pandas has specific functions defined for determining rolling statistics. In [16]:

f, (ax1) = plt.subplots(1, figsize = (12, 8)) moving_avg = ts_log.rolling(7).mean() plt.plot(ts_log) plt.plot(moving_avg, color='red')

Out[16]:

[<matplotlib.lines.Line2D at 0x2713e0dca20>]

The red line shows the rolling mean. Lets subtract this from the original series. Note that since we are taking average of last 7 values, rolling mean is not defined for first 6 values. This can be observed as: In [17]:

ts_log_moving_avg_diff = ts_log - moving_avg ts_log_moving_avg_diff.head(8)

Out[17]:

index 2016-01-04 NaN 2016-01-05 NaN 2016-01-06 NaN 2016-01-07 NaN 2016-01-08 NaN 2016-01-11 NaN 2016-01-12 -0.030009 2016-01-13 -0.005996 Name: open, dtype: float64

Notice the first 6 being Nan. Lets drop these NaN values and check the plots to test stationarity. In [18]:

ts_log_moving_avg_diff.dropna(inplace=True) test_stationary(ts_log_moving_avg_diff)

Results of Dickey-Fuller Test: Test Statistic -7.665761e+00 p-value 1.642882e-11 #Lags Used 1.900000e+01 Number of Observations Used 1.067000e+03 Critical Value (1%) -3.436493e+00 Critical Value (5%) -2.864253e+00 Critical Value (10%) -2.568214e+00 dtype: float64 KPSS Stationarity Test Results ===================================== Test Statistic 0.129 P-value 0.462 Lags 15 ------------------------------------- Trend: Constant Critical Values: 0.74 (1%), 0.46 (5%), 0.35 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. KPSS change trend KPSS Stationarity Test Results ===================================== Test Statistic 0.029 P-value 0.880 Lags 15 ------------------------------------- Trend: Constant and Linear Time Trend Critical Values: 0.22 (1%), 0.15 (5%), 0.12 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. Zivot-Andrews Results ===================================== Test Statistic -7.930 P-value 0.000 Lags 19 ------------------------------------- Trend: Constant Critical Values: -5.28 (1%), -4.81 (5%), -4.57 (10%) Null Hypothesis: The process contains a unit root with a single structural break. Alternative Hypothesis: The process is trend and break stationary. Variance-Ratio Test Results ===================================== Test Statistic -1.691 P-value 0.091 Lags 7 ------------------------------------- Computed with overlapping blocks (de-biased)

We can see that the mean and std variations have small variations with time. Also, the Dickey-Fuller test statistic is less than the 10% critical value, thus the TS is stationary with 90% confidence. We can also take second or third order differences which might get even better results in certain applications.

### Exponentially-weighted moving average¶

In [19]:

f, (ax1) = plt.subplots(1, figsize = (12, 6)) expwighted_avg = ts_log.ewm(halflife=7).mean() plt.plot(ts_log) plt.plot(expwighted_avg, color='red')

Out[19]:

[<matplotlib.lines.Line2D at 0x2713efbaac8>]

Note that here the parameter ‘halflife’ is used to define the amount of exponential decay. This is just an assumption here and would depend largely on the business domain. Other parameters like span and center of mass can also be used to define decay which are discussed in the link shared above. Now, let’s remove this from series and check stationarity: In [20]:

ts_log_ewma_diff = ts_log - expwighted_avg test_stationary(ts_log_ewma_diff)

Results of Dickey-Fuller Test: Test Statistic -6.303045e+00 p-value 3.375343e-08 #Lags Used 1.900000e+01 Number of Observations Used 1.073000e+03 Critical Value (1%) -3.436459e+00 Critical Value (5%) -2.864237e+00 Critical Value (10%) -2.568206e+00 dtype: float64 KPSS Stationarity Test Results ===================================== Test Statistic 0.245 P-value 0.195 Lags 18 ------------------------------------- Trend: Constant Critical Values: 0.74 (1%), 0.46 (5%), 0.35 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. KPSS change trend KPSS Stationarity Test Results ===================================== Test Statistic 0.044 P-value 0.664 Lags 18 ------------------------------------- Trend: Constant and Linear Time Trend Critical Values: 0.22 (1%), 0.15 (5%), 0.12 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. Zivot-Andrews Results ===================================== Test Statistic -6.578 P-value 0.000 Lags 19 ------------------------------------- Trend: Constant Critical Values: -5.28 (1%), -4.81 (5%), -4.57 (10%) Null Hypothesis: The process contains a unit root with a single structural break. Alternative Hypothesis: The process is trend and break stationary. Variance-Ratio Test Results ===================================== Test Statistic -0.977 P-value 0.329 Lags 7 ------------------------------------- Computed with overlapping blocks (de-biased)

## Why and How to smoothen a time series?¶

Smoothening of a time series may be useful in:

Reducing the effect of noise in a signal get a fair approximation of the noise-filtered series. The smoothed version of series can be used as a feature to explain the original series itself. Visualize the underlying trend better So how to smoothen a series? Let’s discuss the following methods:

Take a moving average Do a LOESS smoothing (Localized Regression) Do a LOWESS smoothing (Locally Weighted Regression) Moving average is nothing but the average of a rolling window of defined width. But you must choose the window-width wisely, because, large window-size will over-smooth the series. For example, a window-size equal to the seasonal duration (ex: 12 for a month-wise series), will effectively nullify the seasonal effect.

LOESS, short for ‘LOcalized regrESSion’ fits multiple regressions in the local neighborhood of each point. It is implemented in the statsmodels package, where you can control the degree of smoothing using frac argument which specifies the percentage of data points nearby that should be considered to fit a regression model.

https://www.machinelearningplus.com/time-series/time-series-analysis-python/ In [21]:

from statsmodels.nonparametric.smoothers_lowess import lowess plt.rcParams.update({'xtick.bottom' : False, 'axes.titlepad':5}) # Import df_orig = df['open'] # 1. Moving Average df_ma = df_orig.rolling(7, center=True, closed='both').mean() # 2. Loess Smoothing (5% and 15%) df_loess_5 = pd.DataFrame(lowess(df_orig, np.arange(len(df_orig)), frac=0.05)[:, 1], index=df_orig.index, columns=['value']) df_loess_15 = pd.DataFrame(lowess(df_orig, np.arange(len(df_orig)), frac=0.15)[:, 1], index=df_orig.index, columns=['value']) # Plot fig, axes = plt.subplots(4,1, figsize=(7, 7), sharex=True, dpi=120) df_orig.plot(ax=axes[0], color='k', title='Original Series') df_loess_5.plot(ax=axes[1], title='Loess Smoothed 5%') df_loess_15.plot(ax=axes[2], title='Loess Smoothed 15%') df_ma.plot(ax=axes[3], title='Moving Average (3)') fig.suptitle('How to Smoothen a Time Series', y=0.95, fontsize=14) plt.show()

## Eliminating Trend and Seasonality¶

The simple trend reduction techniques discussed before don’t work in all cases, particularly the ones with high seasonality. Lets discuss two ways of removing trend and seasonality:

- Differencing – taking the differece with a particular time lag
- Decomposition – modeling both trend and seasonality and removing them from the model.

#### Differencing¶

One of the most common methods of dealing with both trend and seasonality is differencing. In this technique, we take the difference of the observation at a particular instant with that at the previous instant. This mostly works well in improving stationarity. First order differencing can be done in Pandas as: In [22]:

f, (ax1) = plt.subplots(1, figsize = (12, 6)) ts_log_diff = ts_log - ts_log.shift(7) plt.plot(ts_log_diff)

Out[22]:

[<matplotlib.lines.Line2D at 0x2713f01b908>]

In [23]:

ts_log_diff

Out[23]:

index 2016-01-04 NaN 2016-01-05 NaN 2016-01-06 NaN 2016-01-07 NaN 2016-01-08 NaN ... 2020-04-30 0.041673 2020-05-01 0.031025 2020-05-04 0.019271 2020-05-05 0.009251 2020-05-06 0.006425 Name: open, Length: 1093, dtype: float64

In [24]:

ts_log_diff.dropna(inplace=True) test_stationary(ts_log_diff)

Results of Dickey-Fuller Test: Test Statistic -6.436710e+00 p-value 1.645978e-08 #Lags Used 2.200000e+01 Number of Observations Used 1.063000e+03 Critical Value (1%) -3.436517e+00 Critical Value (5%) -2.864263e+00 Critical Value (10%) -2.568220e+00 dtype: float64 KPSS Stationarity Test Results ===================================== Test Statistic 0.130 P-value 0.456 Lags 17 ------------------------------------- Trend: Constant Critical Values: 0.74 (1%), 0.46 (5%), 0.35 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. KPSS change trend KPSS Stationarity Test Results ===================================== Test Statistic 0.031 P-value 0.854 Lags 17 ------------------------------------- Trend: Constant and Linear Time Trend Critical Values: 0.22 (1%), 0.15 (5%), 0.12 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. Zivot-Andrews Results ===================================== Test Statistic -6.695 P-value 0.000 Lags 22 ------------------------------------- Trend: Constant Critical Values: -5.28 (1%), -4.81 (5%), -4.57 (10%) Null Hypothesis: The process contains a unit root with a single structural break. Alternative Hypothesis: The process is trend and break stationary. Variance-Ratio Test Results ===================================== Test Statistic 1.289 P-value 0.197 Lags 7 ------------------------------------- Computed with overlapping blocks (de-biased)

#### This does not appear to have reduced trend considerably.¶

The Dickey-Fuller test statistic is not significantly lower than the 1% critical value. So this TS is not very close to stationary. We can try advanced decomposition techniques as well which can generate better results. Also, you should note that converting the residuals into original values for future data in not very intuitive in this case.

### Decomposition¶

We can model the residuals of the the logs. Lets check stationarity of residuals: In [25]:

decomposition = seasonal_decompose(ts_log_diff, model='additive', freq = 52) ts_log_decompose = decomposition.resid ts_log_decompose.dropna(inplace=True) test_stationary(ts_log_decompose)

Results of Dickey-Fuller Test: Test Statistic -9.404658e+00 p-value 6.056794e-16 #Lags Used 2.200000e+01 Number of Observations Used 1.011000e+03 Critical Value (1%) -3.436835e+00 Critical Value (5%) -2.864403e+00 Critical Value (10%) -2.568294e+00 dtype: float64 KPSS Stationarity Test Results ===================================== Test Statistic 0.093 P-value 0.620 Lags 16 ------------------------------------- Trend: Constant Critical Values: 0.74 (1%), 0.46 (5%), 0.35 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. KPSS change trend KPSS Stationarity Test Results ===================================== Test Statistic 0.035 P-value 0.806 Lags 16 ------------------------------------- Trend: Constant and Linear Time Trend Critical Values: 0.22 (1%), 0.15 (5%), 0.12 (10%) Null Hypothesis: The process is weakly stationary. Alternative Hypothesis: The process contains a unit root. Zivot-Andrews Results ===================================== Test Statistic -9.469 P-value 0.000 Lags 22 ------------------------------------- Trend: Constant Critical Values: -5.28 (1%), -4.81 (5%), -4.57 (10%) Null Hypothesis: The process contains a unit root with a single structural break. Alternative Hypothesis: The process is trend and break stationary. Variance-Ratio Test Results ===================================== Test Statistic 0.789 P-value 0.430 Lags 7 ------------------------------------- Computed with overlapping blocks (de-biased)

### Let us see the performance of ts_log_diff on modelling.¶

### How to estimate the forecastability of a time series?¶

The more regular and repeatable patterns a time series has, the easier it is to forecast. The ‘Approximate Entropy’ can be used to quantify the regularity and unpredictability of fluctuations in a time series. The higher the approximate entropy, the more difficult it is to forecast it.

Another better alternate is the ‘Sample Entropy’.

Sample Entropy is similar to approximate entropy but is more consistent in estimating the complexity even for smaller time series. For example, a random time series with fewer data points can have a lower ‘approximate entropy’ than a more ‘regular’ time series, whereas, a longer random time series will have a higher ‘approximate entropy’. Sample Entropy handles this problem nicely. In [26]:

# https://en.wikipedia.org/wiki/Approximate_entropy def ApEn(U, m, r): """Compute Aproximate entropy""" def _maxdist(x_i, x_j): return max([abs(ua - va) for ua, va in zip(x_i, x_j)]) def _phi(m): x = [[U[j] for j in range(i, i + m - 1 + 1)] for i in range(N - m + 1)] C = [len([1 for x_j in x if _maxdist(x_i, x_j) <= r]) / (N - m + 1.0) for x_i in x] return (N - m + 1.0)**(-1) * sum(np.log(C)) N = len(U) return abs(_phi(m+1) - _phi(m)) print(ApEn(ts_log_diff, m=2, r=0.2*np.std(ts_log_diff)))

1.1685669292469725

In [27]:

# https://en.wikipedia.org/wiki/Sample_entropy def SampEn(U, m, r): """Compute Sample entropy""" def _maxdist(x_i, x_j): return max([abs(ua - va) for ua, va in zip(x_i, x_j)]) def _phi(m): x = [[U[j] for j in range(i, i + m - 1 + 1)] for i in range(N - m + 1)] C = [len([1 for j in range(len(x)) if i != j and _maxdist(x[i], x[j]) <= r]) for i in range(len(x))] return sum(C) N = len(U) return -np.log(_phi(m+1) / _phi(m)) print(SampEn(ts_log_diff, m=2, r=0.2*np.std(ts_log_diff)))

1.0896107625328675

### Checking Stationarity (ACF and PACF Plots)¶

Another common method of checking to see if data is stationary is to plot its autocorrelation function.

- Autocorrelation Function (ACF): It is a measure of the correlation between the the TS with a lagged version of itself. For instance at lag 5, ACF would compare series at time instant ‘t1’…’t2’ with series at instant ‘t1-5’…’t2-5’ (t1-5 and t2 being end points).
- Partial Autocorrelation Function (PACF): This measures the correlation between the TS with a lagged version of itself but after eliminating the variations already explained by the intervening comparisons. Eg at lag 5, it will check the correlation but remove the effects already explained by lags 1 to 4.

The autocorrelation involves “sliding” or “shifting” a function and multiplying it with its unshifted self. This allows one to measure the overlap of the function with itself at different points in time. This process ends up being useful for discovering periodicity in ones data. For example, a sine wave repeats itself every 2\pi2π, so one would see peaks in the autocorrelation function every 2\pi2π.

We can use statsmodels to plot our autocorrelation function. We can use the autocorrelation function to quantify this. Autocorrelations always start at 1 (a function perfectly overlaps with itself), but we would like to see that it drops down close to zero. These plots show that it certaintly does not!

Autocorrelation is a problem in regular regressions like above, but we’ll use it to our advantage when we setup an ARIMA model below. The basic idea is pretty sensible: if your regression residuals have a clear pattern, then there’s clearly some structure in the data that you aren’t taking advantage of. If a positive residual today means you’ll likely have a positive residual tomorrow, why not incorporate that information into your forecast, and lower your forecasted value for tomorrow? That’s pretty much what ARIMA does. In [28]:

#ACF and PACF plots: from statsmodels.tsa.stattools import acf, pacf def viz_stationary(timeseries, n_lags): lag_acf = acf(timeseries, nlags=n_lags) lag_pacf = pacf(timeseries, nlags=n_lags, method='ols') f, (ax1) = plt.subplots(1, figsize = (10, 5)) #Plot ACF: plt.subplot(121) plt.plot(lag_acf) plt.axhline(y=0,linestyle='--',color='gray') plt.axhline(y=-1.96/np.sqrt(len(timeseries)),linestyle='--',color='gray') plt.axhline(y=1.96/np.sqrt(len(timeseries)),linestyle='--',color='gray') plt.title('Autocorrelation Function') #Plot PACF: plt.subplot(122) plt.plot(lag_pacf) plt.axhline(y=0,linestyle='--',color='gray') plt.axhline(y=-1.96/np.sqrt(len(timeseries)),linestyle='--',color='gray') plt.axhline(y=1.96/np.sqrt(len(timeseries)),linestyle='--',color='gray') plt.title('Partial Autocorrelation Function') plt.tight_layout() viz_stationary(ts_log_diff,7)

In [29]:

# from pandas.plotting import autocorrelation_plot # f, (ax1) = plt.subplots(1, figsize = (8, 6)) # autocorrelation_plot(ts_log_diff.tolist())

## Forecasting a Time Series¶

### What is an ARIMA model?¶

ARIMA stands for Auto-Regressive Integrated Moving Averages. The ARIMA forecasting for a stationary time series is nothing but a linear (like a linear regression) equation. The predictors depend on the parameters (p,d,q) of the ARIMA model:

- Number of AR (Auto-Regressive) terms (p): AR terms are just lags of dependent variable. For instance if p is 5, the predictors for x(t) will be x(t-1)….x(t-5).
- Number of MA (Moving Average) terms (q): MA terms are lagged forecast errors in prediction equation. For instance if q is 5, the predictors for x(t) will be e(t-1)….e(t-5) where e(i) is the difference between the moving average at ith instant and actual value.
- Number of Differences (d): These are the number of nonseasonal differences, i.e. in this case we took the first order difference. So either we can pass that variable and put d=0 or pass the original variable and put d=1. Both will generate same results.

We determined the value of ‘p’ and ‘q’ using the plots above.

In the plot, the two dotted lines on either sides of 0 are the confidence interevals. These can be used to determine the ‘p’ and ‘q’ values as:

- p – The lag value where the PACF chart crosses the upper confidence interval for the first time. If you notice closely, in this case p=2.
- q – The lag value where the ACF chart crosses the upper confidence interval for the first time. If you notice closely, in this case q=5.
- D, we need to look which lagged version differencing made the series stationary.if X — X.shift(1) makes your X series stationary, D =1.

Now, lets make 3 different ARIMA models considering individual as well as combined effects. I will also print the RSS for each. Please note that here RSS is for the values of residuals and not actual series.

We need to load the ARIMA model first:

- P = Using AutoCorrelation plot for AR
- D = For Integrated term in AR-I-MA
- Q = Using PartialAutoCorrelation plot for MA

### AR Model¶

In [30]:

model = AR(ts_log_diff) results_AR = model.fit(disp=-1) f, (ax1) = plt.subplots(1, figsize = (12, 6)) plt.plot(ts_log_diff[len(ts_log_diff) - len(results_AR.fittedvalues):]) plt.plot(results_AR.fittedvalues, color='red') plt.title('RMSE: %.4f'% np.sqrt(sum((results_AR.fittedvalues- ts_log_diff[len(ts_log_diff) - len(results_AR.fittedvalues):])**2))) plt.show()

### Autoregressive Moving-Average Processes (ARMA) Model¶

In [31]:

model = ARMA(ts_log_diff, order=(2, 1, 5)) results_MA = model.fit(disp=-1) f, (ax1) = plt.subplots(1, figsize = (12, 6)) plt.plot(ts_log_diff) plt.plot(results_MA.fittedvalues, color='red') plt.title('RMSE: %.4f'% np.sqrt(sum((results_MA.fittedvalues-ts_log_diff)**2))) plt.show()

### Combined Model¶

In [32]:

model = ARIMA(ts_log_diff, order=(2, 1, 5)) results_ARIMA = model.fit(disp=-7) f, (ax1) = plt.subplots(1, figsize = (12, 6)) plt.plot(ts_log_diff[len(ts_log_diff) - len(results_ARIMA.fittedvalues):]) plt.plot(results_ARIMA.fittedvalues, color='red') plt.title('RMSE: %.4f'% np.sqrt(sum((results_ARIMA.fittedvalues- ts_log_diff[len(ts_log_diff) - len(results_ARIMA.fittedvalues):])**2))) plt.show()

Here we can see that the ARIMA is not the best model while MA and AR models have almost the same RSS but AR is significantly better.

## Taking it back to original scale¶

Since the AR model gave best result, lets scale it back to the original values and see how well it performs there. First step would be to store the predicted results as a separate series and observe it.

Once you are done with forecasting, don’t forget to trace back all transformation you made to your original series(most common mistake). Additionally, in the same reverse sequence(follow LIFO, Last applied transformation reverted first). Do create a copy of your series. It will help you out in recovering things back. For example, if you applied log() first and then differencing, first add what has been subtracted and than exp() over that. In [33]:

predictions_AR_diff = pd.Series(results_AR.fittedvalues, copy=True) print(predictions_AR_diff.head())

index 2016-02-16 -0.024557 2016-02-17 -0.000758 2016-02-18 0.037416 2016-02-19 0.075096 2016-02-22 0.067324 dtype: float64

Notice that these start from ‘2016-02-16 ’ and not the first day.

This is because we took a lag and first elements doesn’t have anything before it to subtract from. The way to convert the differencing to log scale is to add these differences consecutively to the base number. In [34]:

normal_values = np.exp(predictions_AR_diff) + np.exp(ts_log.shift(7))

### Fill in the null values with the true values¶

In [35]:

predictions_values = normal_values.combine_first(np.exp(ts_log))

In [36]:

predictions_values.head()

Out[36]:

index 2016-01-04 116.91 2016-01-05 116.72 2016-01-06 115.78 2016-01-07 113.63 2016-01-08 112.35 dtype: float64

In [37]:

#predictions_AR_log = np.exp(predictions_AR_log) f, (ax1) = plt.subplots(1, figsize = (12, 6)) plt.plot(df['open'], label = 'true') plt.plot(predictions_values, label = 'predicted') plt.title('RMSE: %.4f'% np.sqrt(sum((predictions_values-df['open'])**2)/len(df['open']))) plt.legend(loc="upper left") plt.show()

Root Mean Square Error (RMSE) is the standard deviation of the residuals (prediction errors). Residuals are a measure of how far from the regression line data points are. It is a measure of how spread out these residuals are. In other words, it tells how concentrated the data is around the line of best fit. Looking at the plot, the model is predicting the values with a lag. In [ ]:

Root Mean Square Error (RMSE) is the standard deviation of the residuals (prediction errors). Residuals are a measure of how far from the regression line data points are. It is a measure of how spread out these residuals are. In other words, it tells how concentrated the data is around the line of best fit. Looking at the plot, the model is predicting the values with a lag. In [ ]: