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.
├── LICENSE ├── Makefile <- Makefile with commands like `make data` or `make train` ├── README.md <- The top-level README for developers using this project. ├── docs <- A default Sphinx project; see sphinx-doc.org 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` │ ├── setup.py <- makes project pip installable (pip install -e .) so src can be imported ├── src <- Source code for use in this project. │ ├── __init__.py <- Makes src a Python module │ │ │ ├── data <- Scripts to download or generate data │ │ └── make_dataset.py │ │ │ ├── features <- Scripts to turn raw data into features for modeling │ │ └── build_features.py │ │ │ ├── models <- Scripts to train models and then use trained models to make │ │ │ predictions │ │ ├── predict_model.py │ │ └── train_model.py │ │ │ └── visualization <- Scripts to create exploratory and results oriented visualizations │ └── visualize.py │ └── tox.ini <- tox file with settings for running tox; see tox.testrun.org
Project based on the cookiecutter data science project template.
Machine learning, statistics, analytics
|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|
|pandas-datareader||Pulls financial data from different sources (e.g. yahoo, google, Quandl)|
Please visit my site for more information.