Boosting X-formers with Structured Matrix for Long Sequence Time Series Forecasting

• URL: http://arxiv.org/abs/2405.12462v3
• Date: Thu, 24 Oct 2024 01:52:17 GMT
• Title: Boosting X-formers with Structured Matrix for Long Sequence Time Series Forecasting
• Authors: Zhicheng Zhang, Yong Wang, Shaoqi Tan, Bowei Xia, Yujie Luo,
• Abstract summary: We propose a novel architectural framework that enhances Transformer models through the integration of Surrogate Attention Blocks (SAB) and Surrogate Feed-Forward Neural Network Blocks (SFB) Experiments on nine Transformer variants across five distinct time series tasks demonstrate an average performance improvement of 9.45%, alongside a 46% reduction in model size.
• Score: 7.3758245014991255
• License:
• Abstract: Transformer-based models for long sequence time series forecasting problems have gained significant attention due to their exceptional forecasting precision. However, the self-attention mechanism introduces challenges in terms of computational efficiency due to its quadratic time complexity. To address these issues, we propose a novel architectural framework that enhances Transformer models through the integration of Surrogate Attention Blocks (SAB) and Surrogate Feed-Forward Neural Network Blocks (SFB). They replace the self-attention and feed-forward layer by leveraging structured matrices that reduce both time and space complexity while maintaining the expressive power of the original self-attention mechanism and feed-forward network. The equivalence of this substitution is fully demonstrated. Extensive experiments on nine Transformer variants across five distinct time series tasks demonstrate an average performance improvement of 9.45%, alongside a 46% reduction in model size. These results confirm the efficacy of our surrogate-based approach in maintaining prediction accuracy while significantly boosting computational efficiency.

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