There are 7 repositories under dynamic-time-warping topic.
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
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
PyTorch implementation of Soft-DTW: a Differentiable Loss Function for Time-Series in CUDA
An implementation of soft-DTW divergences.
Dynamic Time Warping (DTW) and related algorithms in Julia, at Julia speeds
Implementation of soft dynamic time warping in pytorch
Scikit-Learn compatible HMM and DTW based sequence machine learning algorithms in Python.
A fast, scalable and light-weight C++ Fréchet and DTW distance library, exposed to python and focused on clustering of polygonal curves.
Measure the distance between two spectra/signals using optimal transport and related metrics
A simple framework for gesture recognition in Java
Discriminative Prototypes learned by Dynamic Time Warping (DTW) for Time Series Classification (TSC)
wildboar is a Python module for temporal machine learning
Python extension backed by a multi-threaded Rust implementation of Dynamic Time Warping (DTW).
Align signals to each other
R package to compute dissimilarity between multivariate time series
Voice Alignment and Conversion with Neural Networks and the WORLD codec.
Gesture Recognition in RGB Videos Using Human Body Keypoints and Dynamic Time Warping
Sequence clustering using k-means with dynamic time warping (DTW) and Damerau-Levenshtein distance as similarity measures
ICDE 2019 - KV-match: A Subsequence Matching Approach Supporting Normalization and Time Warping
Repository for my Master Thesis at the Computer Vision Laboratory (EPFL).