Treating Brain-inspired Memories as Priors for Diffusion Model to Forecast Multivariate Time Series

TL;DR

This paper introduces a brain-inspired memory-augmented diffusion model for multivariate time series forecasting, leveraging semantic and episodic memories as priors to improve prediction accuracy and robustness across diverse datasets.

Contribution

It proposes a novel brain-inspired memory module integrated with diffusion models, capturing general and specific temporal patterns for multivariate time series forecasting.

Findings

Outperforms existing methods on eight datasets

Effectively captures recurrent temporal patterns across channels

Enhances prediction robustness and accuracy

Abstract

Forecasting Multivariate Time Series (MTS) involves significant challenges in various application domains. One immediate challenge is modeling temporal patterns with the finite length of the input. These temporal patterns usually involve periodic and sudden events that recur across different channels. To better capture temporal patterns, we get inspiration from humans' memory mechanisms and propose a channel-shared, brain-inspired memory module for MTS. Specifically, brain-inspired memory comprises semantic and episodic memory, where the former is used to capture general patterns, such as periodic events, and the latter is employed to capture special patterns, such as sudden events, respectively. Meanwhile, we design corresponding recall and update mechanisms to better utilize these patterns. Furthermore, acknowledging the capacity of diffusion models to leverage memory as a prior, we present a brain-inspired memory-augmented diffusion model. This innovative model retrieves relevant memories for different channels, utilizing them as distinct priors for MTS predictions. This incorporation significantly enhances the accuracy and robustness of predictions. Experimental results on eight datasets consistently validate the superiority of our approach in capturing and leveraging diverse recurrent temporal patterns across different channels.