The machine learning toolkit for time series analysis in Python

Time series distances: Dynamic Time Warping (fast DTW implementation in C)

gesture recognition toolkit

Python implementation of KNN and DTW classification algorithm

Fast CUDA implementation of (differentiable) soft dynamic time warping for PyTorch

Python implementation of soft-DTW.

Transfer learning for time series classification

Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

Quantify the difference between two arbitrary curves in space

This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.

Data augmentation using synthetic data for time series classification with deep residual networks

An implementation of soft-DTW divergences.

PyTorch implementation of Soft-DTW: a Differentiable Loss Function for Time-Series in CUDA

Implementation of soft dynamic time warping in pytorch

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

Scikit-Learn compatible HMM and DTW based sequence machine learning algorithms in Python.

Dynamic time warping for JavaScript

Dynamic Time Warping single header library for C++

Measure the distance between two spectra/signals using optimal transport and related metrics

A fast, scalable and light-weight C++ Fréchet and DTW distance library, exposed to python and focused on clustering of polygonal curves.