Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecasting

TL;DR

This paper introduces DMMV, a multi-modal view framework that leverages trend-seasonal decomposition and large vision models to improve long-term time series forecasting, outperforming existing methods on multiple datasets.

Contribution

The paper proposes a novel decomposition-based multi-modal view framework, DMMV, that effectively integrates trend and seasonal components for enhanced long-term time series forecasting.

Findings

DMMV outperforms 14 SOTA models on 6 of 8 datasets.

DMMV achieves the best mean squared error (MSE) on most benchmarks.

Multi-modal views improve forecasting accuracy over single-view approaches.

Abstract

Time series, typically represented as numerical sequences, can also be transformed into images and texts, offering multi-modal views (MMVs) of the same underlying signal. These MMVs can reveal complementary patterns and enable the use of powerful pre-trained large models, such as large vision models (LVMs), for long-term time series forecasting (LTSF). However, as we identified in this work, the state-of-the-art (SOTA) LVM-based forecaster poses an inductive bias towards "forecasting periods". To harness this bias, we propose DMMV, a novel decomposition-based multi-modal view framework that leverages trend-seasonal decomposition and a novel backcast-residual based adaptive decomposition to integrate MMVs for LTSF. Comparative evaluations against 14 SOTA models across diverse datasets show that DMMV outperforms single-view and existing multi-modal baselines, achieving the best mean squared error (MSE) on 6 out of 8 benchmark datasets. The code for this paper is available at: https://github.com/D2I-Group/dmmv.