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