LATST: Are Transformers Necessarily Complex for Time-Series Forecasting

Dizhen Liang

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arXiv · 2410.23749

LATST: Are Transformers Necessarily Complex for Time-Series Forecasting

Dizhen Liang

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Abstract

Transformer-based architectures have achieved remarkable success in natural language processing and computer vision. However, their performance in multivariate long-term forecasting often falls short compared to simpler linear baselines. Previous research has identified the traditional attention mechanism as a key factor limiting their effectiveness in this domain. To bridge this gap, we introduce LATST, a novel approach designed to mitigate entropy collapse and training instability common challenges in Transformer-based time series forecasting. We rigorously evaluate LATST across multiple real-world multivariate time series datasets, demonstrating its ability to outperform existing state-of-the-art Transformer models. Notably, LATST manages to achieve competitive performance with fewer parameters than some linear models on certain datasets, highlighting its efficiency and effectiveness.

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Dizhen Liang. 2024-10-31. LATST: Are Transformers Necessarily Complex for Time-Series Forecasting. https://arxiv.org/abs/2410.23749

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