Channel Matters: Estimating Channel Influence for Multivariate Time Series

TL;DR

This paper introduces ChInf, a novel influence estimation method that assesses the impact of individual channels in multivariate time series, improving anomaly detection and data pruning tasks.

Contribution

The paper presents the first channel-wise influence estimation method for MTS, enabling better understanding and manipulation of channel effects on model performance.

Findings

ChInf outperforms existing influence functions in MTS anomaly detection.

ChInf-based methods achieve top-1 ranking in MTS data pruning.

ChInf demonstrates effectiveness across multiple MTS analysis tasks.

Abstract

The influence function serves as an efficient post-hoc interpretability tool that quantifies the impact of training data modifications on model parameters, enabling enhanced model performance, improved generalization, and interpretability insights without the need for expensive retraining processes. Recently, Multivariate Time Series (MTS) analysis has become an important yet challenging task, attracting significant attention. While channel extremely matters to MTS tasks, channel-centric methods are still largely under-explored for MTS. Particularly, no previous work studied the effects of channel information of MTS in order to explore counterfactual effects between these channels and model performance. To fill this gap, we propose a novel Channel-wise Influence (ChInf) method that is the first to estimate the influence of different channels in MTS. Based on ChInf,we naturally derived two channel-wise algorithms by incorporating ChInf into classic MTS tasks. Extensive experiments demonstrate the effectiveness of ChInf and ChInf-based methods in critical MTS analysis tasks, such as MTS anomaly detection and MTS data pruning. Specifically, our ChInf-based methods rank top-1 among all methods for comparison, while previous influence functions do not perform well on MTS anomaly detection tasks and MTS data pruning problem. This fully supports the superiority and necessity of ChInf.