LSEAttention is All You Need for Time Series Forecasting

• URL: http://arxiv.org/abs/2410.23749v2
• Date: Fri, 01 Nov 2024 02:47:29 GMT
• Title: LSEAttention is All You Need for Time Series Forecasting
• Authors: Dizhen Liang,
• Abstract summary: Transformer-based architectures have achieved remarkable success in natural language processing and computer vision. I introduce textbfLSEAttention, an approach designed to address entropy collapse and training instability commonly observed in transformer models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer-based architectures have achieved remarkable success in natural language processing and computer vision. However, their performance in multivariate long-term forecasting often lags behind simpler linear baselines. Previous studies have identified the traditional attention mechanism as a significant factor contributing to this limitation. To unlock the full potential of transformers for multivariate time series forecasting, I introduce \textbf{LSEAttention}, an approach designed to address entropy collapse and training instability commonly observed in transformer models. I validate the effectiveness of LSEAttention across various real-world multivariate time series datasets, demonstrating that it not only outperforms existing time series transformer models but also exceeds the performance of some state-of-the-art models on specific datasets.

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