The machine learning toolkit for time series analysis in Python

A toolkit for time series machine learning and deep learning

Implement Reservoir Computing models for time series classification, clustering, forecasting, and much more!

Python implementation of k-Shape

Book and material for the course "Time series analysis with Python" (STA-2003)

Dynamic Time Warping (DTW) and related algorithms in Julia, at Julia speeds

TSrepr: R package for time series representations

Blog about time series data mining in R.

Matlab implementation for k-Shape

Clustering using tslearn for Time Series Data.

A Python library for the fast symbolic approximation of time series

Code used in the paper "Time Series Clustering via Community Detection in Networks"

Graph Embedding for Interpretable Time Series Clustering

2018 UCR Time-Series Archive: Backward Compatibility, Missing Values, and Varying Lengths

COVID-19 spread shiny dashboard with a forecasting model, countries' trajectories graphs, and cluster analysis tools

Different deep learning architectures are implemented for time series classification and prediction purposes.

Sequence clustering using k-means with dynamic time warping (DTW) and Damerau-Levenshtein distance as similarity measures

Extending state-of-the-art Time Series Forecasting with Subsequence Time Series (STS) Clustering to enforce model seasonality adaptation.

FeatTS is a Semi-Supervised Clustering method that leverages features extracted from the raw time series to create clusters that reflect the original time series.

A symbolic time series representation building Brownian bridges