Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection

TL;DR

This paper introduces RobustDTW, a new time series dissimilarity measure that effectively handles noise and outliers, improves computational efficiency, and enhances periodicity and outlier detection in real-world datasets.

Contribution

The paper proposes RobustDTW, a novel dissimilarity measure for time series that incorporates trend estimation and multi-level refinement to improve robustness and efficiency.

Findings

RobustDTW outperforms existing DTW variants in outlier detection.

RobustDTW achieves superior periodicity detection accuracy.

The multi-level framework reduces computational complexity.

Abstract

Dynamic time warping (DTW) is an effective dissimilarity measure in many time series applications. Despite its popularity, it is prone to noises and outliers, which leads to singularity problem and bias in the measurement. The time complexity of DTW is quadratic to the length of time series, making it inapplicable in real-time applications. In this paper, we propose a novel time series dissimilarity measure named RobustDTW to reduce the effects of noises and outliers. Specifically, the RobustDTW estimates the trend and optimizes the time warp in an alternating manner by utilizing our designed temporal graph trend filtering. To improve efficiency, we propose a multi-level framework that estimates the trend and the warp function at a lower resolution, and then repeatedly refines them at a higher resolution. Based on the proposed RobustDTW, we further extend it to periodicity detection and outlier time series detection. Experiments on real-world datasets demonstrate the superior performance of RobustDTW compared to DTW variants in both outlier time series detection and periodicity detection.