Contrastive Multivariate Singular Spectrum Analysis

TL;DR

The paper presents a new unsupervised technique called Contrastive Multivariate Singular Spectrum Analysis for better signal decomposition in time series, emphasizing signals relevant to analysts rather than just variance.

Contribution

It introduces a contrastive approach to multivariate SSA that leverages background data to highlight important signals in target time series.

Findings

Effective in synthetic examples

Improves clustering of ECG signals

Focuses on relevant signals rather than variance

Abstract

We introduce Contrastive Multivariate Singular Spectrum Analysis, a novel unsupervised method for dimensionality reduction and signal decomposition of time series data. By utilizing an appropriate background dataset, the method transforms a target time series dataset in a way that evinces the sub-signals that are enhanced in the target dataset, as opposed to only those that account for the greatest variance. This shifts the goal from finding signals that explain the most variance to signals that matter the most to the analyst. We demonstrate our method on an illustrative synthetic example, as well as show the utility of our method in the downstream clustering of electrocardiogram signals from the public MHEALTH dataset.