# ClaSP -- Parameter-free Time Series Segmentation

arXiv: https://arxiv.org/abs/2207.13987 · HTML: https://mlsanity.com/paper/2207.13987/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 61 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [A Time Series Forest for Classification and Feature Extraction](https://mlsanity.com/paper/1302.2277/) (2013)
- [An Evaluation of Change Point Detection Algorithms](https://mlsanity.com/paper/2003.06222/) (2020)
- [The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version](https://mlsanity.com/paper/1602.01711/) (2016)
- [Self-supervised Learning from a Multi-view Perspective](https://mlsanity.com/paper/2006.05576/) (2020)
- [Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset](https://mlsanity.com/paper/2103.08590/) (2021)
- [ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data](https://mlsanity.com/paper/2008.03230/) (2020)

## Built on by (most cited first, 21 of 21)

- [aeon: a Python toolkit for learning from time series](https://mlsanity.com/paper/2406.14231/) (2024)
- [Universal Time-Series Representation Learning: A Survey](https://mlsanity.com/paper/2401.03717/) (2024)
- [E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series](https://mlsanity.com/paper/2402.14041/) (2024)
- [Open Challenges in Time Series Anomaly Detection: An Industry Perspective](https://mlsanity.com/paper/2502.05392/) (2025)
- [Raising the ClaSS of Streaming Time Series Segmentation](https://mlsanity.com/paper/2310.20431/) (2023)
- [CLaP -- State Detection from Time Series](https://mlsanity.com/paper/2504.01783/) (2025)
- [Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification](https://mlsanity.com/paper/2408.00041/) (2024)
- [Penalty Learning for Optimal Partitioning using Multilayer Perceptron](https://mlsanity.com/paper/2408.00856/) (2024)
- [Review of Data-centric Time Series Analysis from Sample, Feature, and Period](https://mlsanity.com/paper/2404.16886/) (2024)
- [An Empirical Evaluation of Factors Affecting SHAP Explanation of Time Series Classification](https://mlsanity.com/paper/2509.03649/) (2025)
- [Toward Interpretable Evaluation Measures for Time Series Segmentation](https://mlsanity.com/paper/2510.23261/) (2025)
- [Are uGLAD? Time will tell!](https://mlsanity.com/paper/2303.11647/) (2023)
- [Ego-Network Transformer for Subsequence Classification in Time Series Data](https://mlsanity.com/paper/2311.02561/) (2023)
- [Acquiring Better Load Estimates by Combining Anomaly and Change Point Detection in Power Grid Time-series Measurements](https://mlsanity.com/paper/2405.16164/) (2024)
- [Learning Satellite Pattern-of-Life Identification: A Diffusion-based Approach](https://mlsanity.com/paper/2412.10814/) (2024)
- [Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data](https://mlsanity.com/paper/2412.11800/) (2024)
- [Learning Penalty for Optimal Partitioning via Automatic Feature Extraction](https://mlsanity.com/paper/2505.07413/) (2025)
- [A Network Science Approach to Granular Time Series Segmentation](https://mlsanity.com/paper/2505.17640/) (2025)
- [Unsupervised Discovery of Long-Term Spatiotemporal Periodic Workflows in Human Activities](https://mlsanity.com/paper/2511.14945/) (2025)
- [High-Dimensional Online Change Point Detection with Adaptive Thresholding and Interpretability](https://mlsanity.com/paper/2609.24278/) (2026)
- [Towards Universal Representation-Based Process Control](https://mlsanity.com/paper/2609.30790/) (2026)

## Abstract (arXiv)

The study of natural and human-made processes often results in long sequences of temporally-ordered values, aka time series (TS). Such processes often consist of multiple states, e.g. operating modes of a machine, such that state changes in the observed processes result in changes in the distribution of shape of the measured values. Time series segmentation (TSS) tries to find such changes in TS post-hoc to deduce changes in the data-generating process. TSS is typically approached as an unsupervised learning problem aiming at the identification of segments distinguishable by some statistical property. Current algorithms for TSS require domain-dependent hyper-parameters to be set by the user, make assumptions about the TS value distribution or the types of detectable changes which limits their applicability. Common hyperparameters are the measure of segment homogeneity and the number of change points, which are particularly hard to tune for each data set. We present ClaSP, a novel, highly accurate, hyper-parameter-free and domain-agnostic method for TSS. ClaSP hierarchically splits a TS into two parts. A change point is determined by training a binary TS classifier for each possible split point and selecting the one split that is best at identifying subsequences to be from either of the partitions. ClaSP learns its main two model-parameters from the data using two novel bespoke algorithms. In our experimental evaluation using a benchmark of 107 data sets, we show that ClaSP outperforms the state of the art in terms of accuracy and is fast and scalable. Furthermore, we highlight properties of ClaSP using several real-world case studies.