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In this project, I use different concepts and packages in the python ecosystem to work with time series data. The aim is to develop an end-to-end machine learning pipeline.

Project Organization

├── Makefile           <- Makefile with commands like `make data` or `make train`
├──          <- The top-level README for developers using this project.
├── docs               <- A default Sphinx project; see for details
├── models             <- Trained and serialized models, model predictions, or model summaries
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
├──           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├──    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └──
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └──
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├──
│   │   └──
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └──
└── tox.ini            <- tox file with settings for running tox; see

Project based on the cookiecutter data science project template.

Machine learning, statistics, analytics


Project Name Description
arch Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for financial econometrics
Featuretools Time series feature extraction, with possible conditionality on other variables with a pandas compatible relational-database-like data container
ffn financial function library
flint A Time Series Library for Apache Spark
HMMLearn Hidden Markov Models with scikit-learn compatible API
prophet Time series forecasting for time series data that has multiple seasonality with linear or non-linear growth
pyts Contains time series preprocessing, transformation as well as classification techniques
skits SciKit-learn-Inspired Time Series models
sktime A scikit-learn compatible library for learning with time series/panel data including time series classification/regression and (supervised/panel) forecasting
statsmodels Contains a submodule for classical time series models and hypothesis tests
stumpy Calculates matrix profile for time series subsequence all-pairs-similarity-search. Offers anomaly detection and pattern (or “motif”) discovery at the same time.
tsfresh Extracts and filters features from time series, allowing supervised classificators and regressor to be applied to time series data
tslearn Direct time series classifiers and regressors


Project name Description
pandas-datareader Pulls financial data from different sources (e.g. yahoo, google, Quandl)

Please visit my site for more information.


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