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 machine learning from time series

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).

skchange provides sktime-compatible change detection and changepoint-based anomaly detection algorithms

📦 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

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

An easy-to-use app that provides 1) interactive visualization of EEG, EMG, and/or Norepinephrine recordings of mice, 2) automated sleep scoring, 3) allows user annotation or correction, 4) post-sleep-scoring statistics about sleep scores.

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 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.

The Unified Framework offers a logical structure that connects concepts from black hole genesis to artificial intelligence safety. Explore foundational theories and key papers to deepen your understanding of reality's framework. 🛠️🌌

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.