VARMA-Enhanced Transformer for Time Series Forecasting

2025-09-04

AI Summary

To address the challenge of effectively modeling local temporal dependencies in Transformer-based models, this paper proposes VARMAformer—a novel time-series forecasting model that integrates classical Vector Autoregressive Moving-Average (VARMA) statistical principles with a cross-attention architecture. Its key contributions include: (1) a VARMA-inspired feature extractor that explicitly captures autoregressive (AR) and moving-average (MA) dynamics; (2) a time-gated attention mechanism that enhances query sensitivity to local temporal context; and (3) a sequence chunking strategy to improve computational efficiency. Extensive experiments on multiple benchmark datasets demonstrate that VARMAformer consistently outperforms state-of-the-art methods in both prediction accuracy and inference efficiency, achieving a superior trade-off between the two. These results validate the effectiveness and necessity of incorporating well-established statistical modeling paradigms into modern deep learning frameworks for time-series analysis.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Diffusion Models for Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Transformer-based models have significantly advanced time series forecasting. Recent work, like the Cross-Attention-only Time Series transformer (CATS), shows that removing self-attention can make the model more accurate and efficient. However, these streamlined architectures may overlook the fine-grained, local temporal dependencies effectively captured by classical statistical models like Vector AutoRegressive Moving Average model (VARMA). To address this gap, we propose VARMAformer, a novel architecture that synergizes the efficiency of a cross-attention-only framework with the principles of classical time series analysis. Our model introduces two key innovations: (1) a dedicated VARMA-inspired Feature Extractor (VFE) that explicitly models autoregressive (AR) and moving-average (MA) patterns at the patch level, and (2) a VARMA-Enhanced Attention (VE-atten) mechanism that employs a temporal gate to make queries more context-aware. By fusing these classical insights into a modern backbone, VARMAformer captures both global, long-range dependencies and local, statistical structures. Through extensive experiments on widely-used benchmark datasets, we demonstrate that our model consistently outperforms existing state-of-the-art methods. Our work validates the significant benefit of integrating classical statistical insights into modern deep learning frameworks for time series forecasting.

Problem

• Integrating classical VARMA patterns into modern Transformer models
• Addressing overlooked local temporal dependencies in time series
• Enhancing cross-attention frameworks with statistical time series analysis

Innovation

• VARMA-inspired Feature Extractor for patch-level patterns
• VARMA-Enhanced Attention with temporal gate mechanism
• Cross-attention-only framework fused with classical statistical principles