ClaSP: parameter-free time series segmentation

DOI10.1007/s10618-023-00923-xzbMath1521.62145arXiv2207.13987OpenAlexW4320913142WikidataQ126176608 ScholiaQ126176608MaRDI QIDQ6040514

Ulf Leser, Patrick Schäfer, Arik Ermshaus

Publication date: 17 May 2023

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2207.13987

zbMATH Keywords

change pointsunsupervised learningsegmentationself-supervised learning

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• Unnamed Item
• A time series forest for classification and feature extraction
• Gaussian process for nonstationary time series prediction
• On tests for detecting change in mean
• The BOSS is concerned with time series classification in the presence of noise
• Technical note: Some properties of splitting criteria
• ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels
• Domain agnostic online semantic segmentation for multi-dimensional time series
• Graph-based change-point detection
• A Cluster Analysis Method for Grouping Means in the Analysis of Variance
• Optimal Detection of Changepoints With a Linear Computational Cost
• Sequential change‐point detection based on direct density‐ratio estimation

This page was built for publication: ClaSP: parameter-free time series segmentation