Accurate and Efficient Multi-Channel Time Series Forecasting via Sparse Attention Mechanism

2026-03-19

AI Summary

This work addresses the challenge of modeling complex intra- and inter-channel dynamic dependencies in multivariate time series forecasting, a task often hindered by insufficient consideration of channel interactions in existing methods. To this end, the authors propose Li-Net, a novel architecture that integrates multimodal embeddings to guide sparse attention toward critical time steps and feature channels. The design further incorporates dynamic representation compression, a configurable nonlinear module, and a multi-scale sparse Top-K Softmax attention mechanism. Evaluated on multiple real-world benchmark datasets, Li-Net achieves state-of-the-art forecasting performance while significantly reducing computational overhead, memory consumption, and inference latency compared to prevailing approaches.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

The task of multi-channel time series forecasting is ubiquitous in numerous fields such as finance, supply chain management, and energy planning. It is critical to effectively capture complex dynamic dependencies within and between channels for accurate predictions. However, traditional method paid few attentions on learning the interaction among channels. This paper proposes Linear-Network (Li-Net), a novel architecture designed for multi-channel time series forecasting that captures the linear and non-linear dependencies among channels. Li-Net dynamically compresses representations across sequence and channel dimensions, processes the information through a configurable non-linear module and subsequently reconstructs the forecasts. Moreover, Li-Net integrates a sparse Top-K Softmax attention mechanism within a multi-scale projection framework to address these challenges. A core innovation is its ability to seamlessly incorporate and fuse multi-modal embeddings, guiding the sparse attention process to focus on the most informative time steps and feature channels. Through the experiment results on multiple real-world benchmark datasets demonstrate that Li-Net achieves competitive performance compared to state-of-the-art baseline methods. Furthermore, Li-Net provides a superior balance between prediction accuracy and computational burden, exhibiting significantly lower memory usage and faster inference times. Detailed ablation studies and parameter sensitivity analyses validate the effectiveness of each key component in our proposed architecture. Keywords: Multivariate Time Series Forecasting, Sparse Attention Mechanism, Multimodal Information Fusion, Non-linear relationship

Problem

• Multivariate Time Series Forecasting
• Sparse Attention Mechanism
• Multimodal Information Fusion
• Non-linear relationship

Innovation

• Sparse Attention Mechanism
• Multimodal Information Fusion
• Multi-channel Time Series Forecasting
• Non-linear Relationship Modeling
• Efficient Inference