time-series-segmentation

A unified framework for machine learning with time series

STUMPY is a powerful and scalable Python library for modern time series analysis

A toolkit for time series machine learning and deep learning

This repository contains a reading list of papers on Time Series Segmentation. This repository is still being continuously improved.

ClaSPy: A Python package for time series segmentation.

This repository contains the time series segmentation benchmark (TSSB).

A python library for fast and flexible changepoint detection

📦 A Python package for online changepoint detection, implementing state-of-the-art algorithms and a novel approach based on neural networks.

This is the supporting website for the paper "MOSAD – a new data set for mobile sensing of human activities".

Time Series Segmentation Algorithms

Desktop app for scoring mouse sleep from EEG, EMG, and norepinephrine recordings — visualize, auto-score with a neural network, correct by hand, and export sleep statistics.

Clustering and segmentation of heterogeneous functional data (sequential data) with regime changes by mixture of Hidden Markov Model Regressions (MixFHMMR) and the EM algorithm

A Python pipeline for segmenting financial assets using unsupervised learning models like K-Means and GMM. This project evaluates clustering configurations not only on performance (Silhouette Score) but also on their statistical stability across train, test, and validation sets using the Wasserstein distance.

Run the uncensored Qwen3.6-27B-AEON model on Apple Silicon hardware using native MLX optimization.

Find changepoints in the correlation and covariance matrices of a multivariate time series and in the mean and variance of a univariate time series

Clustering and segmentation of heteregeneous functional data (sequential data) by mixture of gaussian Hidden Markov Models (MixFHMMs) and the EM algorithm

AI models applied to temporal series. Subject from UPM´s master degree in Machine Learning and Big Data

Codes for generating step length and turning angle series from relocation time series track, their segmentation, and clustering to extract StaMEs, as performed in https://doi.org/10.1101/2023.12.27.573450.

Codes for segmenting and clustering of relocation time series data to generate StaMEs and CAMs, coding of raw CAMs with StaMEs as bases, CAM rectification, and comparison of coding schemes, as performed in https://doi.org/10.1101/2024.08.02.606194.

Automatic changepoint detection (PELT, RBF-kernel cost) for multivariate 1-D signal segmentation, with permutation-based significance testing. Python: NumPy, pandas, ruptures, PyGMT.