Deep Learning for Multi-Scale Changepoint Detection in Multivariate Time Series
This addresses the need for automated changepoint detection in domains like health, where existing methods are limited by user-specified models and inability to handle multi-scale changes, representing a novel method for a known bottleneck.
The paper tackles the problem of detecting changepoints in multivariate time series, which are critical for identifying events like illness onset, by proposing a deep neural network architecture that achieves higher accuracy than state-of-the-art methods in detecting both abrupt and gradual changes at multiple timescales.
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.