# CLaP -- State Detection from Time Series

arXiv: https://arxiv.org/abs/2504.01783 · HTML: https://mlsanity.com/paper/2504.01783/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 73 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [Bake off redux: a review and experimental evaluation of recent time series classification algorithms](https://mlsanity.com/paper/2304.13029/) (2023)
- [An Evaluation of Change Point Detection Algorithms](https://mlsanity.com/paper/2003.06222/) (2020)
- [HYDRA: Competing convolutional kernels for fast and accurate time series classification](https://mlsanity.com/paper/2203.13652/) (2022)
- [Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data](https://mlsanity.com/paper/1706.03161/) (2017)
- [Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets](https://mlsanity.com/paper/2109.13514/) (2021)
- [ClaSP -- Parameter-free Time Series Segmentation](https://mlsanity.com/paper/2207.13987/) (2022)
- [Motiflets -- Simple and Accurate Detection of Motifs in Time Series](https://mlsanity.com/paper/2206.03735/) (2022)
- [A Review and Evaluation of Elastic Distance Functions for Time Series Clustering](https://mlsanity.com/paper/2205.15181/) (2022)
- [Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation](https://mlsanity.com/paper/2001.06386/) (2020)
- [E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series](https://mlsanity.com/paper/2402.14041/) (2024)
- [Hercules Against Data Series Similarity Search](https://mlsanity.com/paper/2212.13297/) (2022)
- [Raising the ClaSS of Streaming Time Series Segmentation](https://mlsanity.com/paper/2310.20431/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Network Science Approach to Granular Time Series Segmentation](https://mlsanity.com/paper/2505.17640/) (2025)
- [BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series](https://mlsanity.com/paper/2608.00513/) (2026)
- [Rondo: Unsupervised Discovery of Recurring Temporal Structure](https://mlsanity.com/paper/2609.32500/) (2026)

## Abstract (arXiv)

The ever-growing amount of sensor data from machines, smart devices, and the environment leads to an abundance of high-resolution, unannotated time series (TS). These recordings encode recognizable properties of latent states and transitions from physical phenomena that can be modelled as abstract processes. The unsupervised localization and identification of these states and their transitions is the task of time series state detection (TSSD). Current TSSD algorithms employ classical unsupervised learning techniques, to infer state membership directly from feature space. This limits their predictive power, compared to supervised learning methods, which can exploit additional label information. We introduce CLaP, a new, highly accurate and efficient algorithm for TSSD. It leverages the predictive power of time series classification for TSSD in an unsupervised setting by applying novel self-supervision techniques to detect whether data segments emerge from the same state. To this end, CLaP cross-validates a classifier with segment-labelled subsequences to quantify confusion between segments. It merges labels from segments with high confusion, representing the same latent state, if this leads to an increase in overall classification quality. We conducted an experimental evaluation using 405 TS from five benchmarks and found CLaP to be significantly more precise in detecting states than six state-of-the-art competitors. It achieves the best accuracy-runtime tradeoff and is scalable to large TS. We provide a Python implementation of CLaP, which can be deployed in TS analysis workflows.