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
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 aming for non uniformly sampled signals |
fecon235 |
Computational tools for financial economics |
hctsa |
Matlab based feature extraction which can be controlled from python |
Nitime |
Timeseries analysis for neuroscience 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 |
statsmodels |
Econometrics package has 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 |
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 |
Examples or singular models
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 |
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 |