The python ecosystem contains different packages that can be used to process time series.
The following list is by no means exhaustive, feel free to submit a pr if you miss something.
| Project Name | Description |
|---|---|
| Arrow | A sensible, human-friendly approach to creating, manipulating, formatting and converting dates, times, and timestamps |
| cesium | Time series platform with feature extraction aiming for non uniformly sampled signals |
| GENDIS | Shapelet discovery by genetic algorithms |
| fecon235 | Computational tools for financial economics |
| hctsa | Matlab based feature extraction which can be controlled from python |
| Nitime | Timeseries analysis for neuroscience data |
| Pastas | Timeseries analysis for hydrological data |
| prophet | Time series forecasting for time series data that has multiple seasonality with linear or non-linear growth |
| pyDSE | ARMA models for Dynamic System Estimation |
| PyFlux | Classical time series forecasting models |
| pyramid | port of R's auto.arima method to Python |
| statsmodels | Contains a submodule for classical time series models and hypothesis tests |
| TensorFlow-Time-Series-Examples | Time Series Prediction with tf.contrib.timeseries |
| Traces | A library for unevenly-spaced time series analysis |
| ta-lib | Calculate technical indicators for financial time series (python wrapper around TA-Lib) |
| ta | Calculate technical indicators for financial time series |
| 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 |
| tspreprocess | Preprocess time series (resampling, denoising etc.), still WIP |
| Project Name | Description |
|---|---|
| LSTM-Neural-Network-for-Time-Series-Prediction | LSTM based forecasting model |
| LSTM_tsc | An LSTM based time-series classification neural network |
| shapelets-python | Shapelet Classifier based on a multi layer neural network |
| UCR_Time_Series_Classification_Deep_Learning_Baseline | Fully Convolutional Neural Networks for state-of-the-art time series classification |
| WTTE-RNN | Time to Event forecast by RNN based Weibull density estimation |
| Project Name | Description |
|---|---|
| ecmwf_models | Readers and converters for climate reanalysis data |
| pandas-datareader | Pulls financial data from different sources (e.g. yahoo, google, Quandl) |
| Project Name | Description |
|---|---|
| artic | High performance datastore for time series and tick data |
| automl_service | Fully automated time series classification pipeline, deployed as a web service |
| cesium | Time series platform with feature extraction aming for non uniformly sampled signals |
| thunder | scalable analysis of image and time series data in python based on spark |
| whisper | File-based time-series database format |
We would like to trigger a homogenization of the formats which are used in the python time series community, please see the concept page