Using python to work with time series data

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.

Machine learning, statistics, analytics

Libraries

Project Name	Description
PyFlux	Classical time series forecasting models
tsfresh	Extracts and filters features from time series, allowing supervised classificators and regressor to be applied to time series data
tspreprocess	Preprocess time series (resampling, denoising etc.), still WIP
tslearn	Direct time series classifiers and regressors
cesium	Time series platform with feature extraction aming for non uniformly sampled signals
hctsa	Matlab based feature extraction which can be controlled from python
statsmodels	Econometrics package has a submodule for classical time series models and hypothesis tests
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
fecon235	Computational tools for financial economics
Nitime	Timeseries analysis for neuroscience data

Examples or singular models

Project Name	Description
LSTM-Neural-Network-for-Time-Series-Prediction	LSTM based forecasting model
UCR_Time_Series_Classification_Deep_Learning_Baseline	Fully Convolutional Neural Networks for state-of-the-art time series classification

Data sets

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)

Databases, frameworks

Project Name	Description
cesium	Time series platform with feature extraction aming for non uniformly sampled signals
whisper	File-based time-series database format