# Deep Learning for Multi-Scale Changepoint Detection in Multivariate Time Series

arXiv: https://arxiv.org/abs/1905.06913 · HTML: https://mlsanity.com/paper/1905.06913/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2019.

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

- [Memory-free Online Change-point Detection: A Novel Neural Network Approach](https://mlsanity.com/paper/2207.03932/) (2022)
- [Time Scale Network: A Shallow Neural Network For Time Series Data](https://mlsanity.com/paper/2311.06170/) (2023)
- [Neural Total Variation Distance Estimators for Changepoint Detection in News Data](https://mlsanity.com/paper/2506.18764/) (2025)

## Abstract (arXiv)

Many real-world time series, such as in health, have changepoints where the system's structure or parameters change. Since changepoints can indicate critical events such as onset of illness, it is highly important to detect them. However, existing methods for changepoint detection (CPD) often require user-specified models and cannot recognize changes that occur gradually or at multiple time-scales. To address both, we show how CPD can be treated as a supervised learning problem, and propose a new deep neural network architecture to efficiently identify both abrupt and gradual changes at multiple timescales from multivariate data. Our proposed pyramid recurrent neural network (PRN) provides scale-invariance using wavelets and pyramid analysis techniques from multi-scale signal processing. Through experiments on synthetic and real-world datasets, we show that PRN can detect abrupt and gradual changes with higher accuracy than the state of the art and can extrapolate to detect changepoints at novel scales not seen in training.