Topological Analysis for Detecting Anomalies (TADA) in Time Series

TL;DR

This paper presents a topological data analysis method for detecting global anomalies in multivariate time series, demonstrating its scalability and effectiveness over existing techniques through extensive experiments.

Contribution

It introduces a novel topological approach for anomaly detection in time series, with theoretical guarantees and improved detection of correlation structure changes.

Findings

More effective at detecting global correlation changes

Scalable to large datasets

Theoretical guarantees for quantization algorithms

Abstract

This paper introduces new methodology based on the field of Topological Data Analysis for detecting anomalies in multivariate time series, that aims to detect global changes in the dependency structure between channels. The proposed approach is lean enough to handle large scale datasets, and extensive numerical experiments back the intuition that it is more suitable for detecting global changes of correlation structures than existing methods. Some theoretical guarantees for quantization algorithms based on dependent time sequences are also provided.