MTS-UNMixers: Multivariate Time Series Forecasting via Channel-Time Dual Unmixing

TL;DR

The paper introduces MTS-UNMixers, a novel dual unmixing network for multivariate time series forecasting that decomposes series into bases and coefficients across time and channels, improving interpretability and accuracy.

Contribution

It proposes a channel-time dual unmixing approach that enhances interpretability and forecasting performance in multivariate time series analysis.

Findings

Significantly outperforms existing methods on benchmark datasets.

Effectively captures long-range dependencies and channel correlations.

Provides physically interpretable representations.

Abstract

Multivariate time series data provide a robust framework for future predictions by leveraging information across multiple dimensions, ensuring broad applicability in practical scenarios. However, their high dimensionality and mixing patterns pose significant challenges in establishing an interpretable and explicit mapping between historical and future series, as well as extracting long-range feature dependencies. To address these challenges, we propose a channel-time dual unmixing network for multivariate time series forecasting (named MTS-UNMixer), which decomposes the entire series into critical bases and coefficients across both the time and channel dimensions. This approach establishes a robust sharing mechanism between historical and future series, enabling accurate representation and enhancing physical interpretability. Specifically, MTS-UNMixers represent sequences over time as a mixture of multiple trends and cycles, with the time-correlated representation coefficients shared across both historical and future time periods. In contrast, sequence over channels can be decomposed into multiple tick-wise bases, which characterize the channel correlations and are shared across the whole series. To estimate the shared time-dependent coefficients, a vanilla Mamba network is employed, leveraging its alignment with directional causality. Conversely, a bidirectional Mamba network is utilized to model the shared channel-correlated bases, accommodating noncausal relationships. Experimental results show that MTS-UNMixers significantly outperform existing methods on multiple benchmark datasets. The code is available at https://github.com/ZHU-0108/MTS-UNMixers.