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 edit the list (will propose a file change via PR) if you miss anything.
| 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 |
| glm-sklearn | scikit-learn compatible wrapper around the GLM module in statsmodels |
| Featuretools | Time series feature extraction, with possible conditionality on other variables with a pandas compatible relational-database-like data container |
| fecon235 | Computational tools for financial economics |
| ffn | financial function library |
| flint | A Time Series Library for Apache Spark |
| hctsa | Matlab based feature extraction which can be controlled from python |
| HMMLearn | Hidden Markov Models with scikit-learn compatible API |
| khiva-python | A Time Series library with accelerated analytics on GPUS, it provides feature extraction and motif discovery among other functionalities. |
| matrixprofile-ts | Python implementation of the Matrix Profile algorithm which offers anomaly detection and pattern (or “motif”) discovery at the same time. |
| 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 |
| pyFTS | Fuzzy set rule-based models for time series forecasting, including multi-step, point, interval and probabilistic forecasting |
| PyFlux | Classical time series forecasting models |
| pysf | A scikit-learn compatible machine learning library for supervised/panel forecasting |
| pyramid | port of R's auto.arima method to Python |
| pyts | Contains time series preprocessing, transformation as well as classification techniques |
| seglearn | Extends the scikit-learn pipeline concept to sequence data |
| 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 |
| TensorFlow-Time-Series-Examples | Time Series Prediction with tf.contrib.timeseries |
| tensorflow_probability.sts | Bayesian Structural Time Series model in Tensorflow Probability |
| 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 |
|---|---|
| ES-RNN forecasting algorithm | Python implementation of the winning forecasting method of the M4 competition combining exponential smoothing with a recurrent neural network using PyTorch |
| Deep learning methods for time series classification | A collection of common deep learning architectures for time series classification |
| 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 |
| M4 competition | Collection of statistical and machine learning forecasting methods |
| 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 |
|---|---|
| Featuretools | Time series feature extraction, with possible conditionality on other variables with a pandas compatible relational-database-like data container |
| pysf | A scikit-learn compatible library for supervised forecasting |
| xarray | Labelled, multi-dimensional data structures as long as they have a shared time index |
| xpandas | Labelled 1D and 2D data container for storing type-heterogeneous tabular data of any type, including time series, and encapsulates feature extraction and transformation modelling in an sklearn-compatible transformer interface, work in progress. |
| Project Name | Description |
|---|---|
| awesome-public-datasets | This huge list of public datasets also has a section on time series datasets |
| ecmwf_models | Readers and converters for climate reanalysis data |
| M4 competition | Forecasting competition on 100,000 time series |
| pandas-datareader | Pulls financial data from different sources (e.g. yahoo, google, Quandl) |
| Timeseriesclassification.com | An extensive repository for time series classification datasets |
| 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 |
| Project Name | Description |
|---|---|
| Time Series Forecasting | Udacity free course to learn about how to build and apply time series analysis/forecasting in business contexts |
We would like to trigger a homogenization of the formats which are used in the python time series community, please see the concept page