Active Learning for Multiple Change Point Detection in Non-stationary Time Series with Deep Gaussian Processes

TL;DR

This paper introduces a novel active learning approach combined with deep Gaussian processes and spectral analysis to improve multiple change point detection in non-stationary time series, enhancing accuracy and efficiency.

Contribution

It presents a new algorithm integrating active learning with deep Gaussian processes and spectral methods for robust, adaptable change point detection in complex time series.

Findings

Outperforms existing methods in detection accuracy

Improves sampling efficiency in change point detection

Effectively adapts to diverse spectral change behaviors

Abstract

Multiple change point (MCP) detection in non-stationary time series is challenging due to the variety of underlying patterns. To address these challenges, we propose a novel algorithm that integrates Active Learning (AL) with Deep Gaussian Processes (DGPs) for robust MCP detection. Our method leverages spectral analysis to identify potential changes and employs AL to strategically select new sampling points for improved efficiency. By incorporating the modeling flexibility of DGPs with the change-identification capabilities of spectral methods, our approach adapts to diverse spectral change behaviors and effectively localizes multiple change points. Experiments on both simulated and real-world data demonstrate that our method outperforms existing techniques in terms of detection accuracy and sampling efficiency for non-stationary time series.