FreEformer: Frequency Enhanced Transformer for Multivariate Time Series Forecasting

• URL: http://arxiv.org/abs/2501.13989v1
• Date: Thu, 23 Jan 2025 08:53:45 GMT
• Title: FreEformer: Frequency Enhanced Transformer for Multivariate Time Series Forecasting
• Authors: Wenzhen Yue, Yong Liu, Xianghua Ying, Bowei Xing, Ruohao Guo, Ji Shi,
• Abstract summary: This paper presents textbfFreEformer, a simple yet effective model that leverages a textbfFrequency textbfEnhanced Transtextbfformer.<n>Experiments demonstrate that FreEformer consistently outperforms state-of-the-art models on eighteen real-world benchmarks.
• Score: 17.738942892605234
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents \textbf{FreEformer}, a simple yet effective model that leverages a \textbf{Fre}quency \textbf{E}nhanced Trans\textbf{former} for multivariate time series forecasting. Our work is based on the assumption that the frequency spectrum provides a global perspective on the composition of series across various frequencies and is highly suitable for robust representation learning. Specifically, we first convert time series into the complex frequency domain using the Discrete Fourier Transform (DFT). The Transformer architecture is then applied to the frequency spectra to capture cross-variate dependencies, with the real and imaginary parts processed independently. However, we observe that the vanilla attention matrix exhibits a low-rank characteristic, thus limiting representation diversity. This could be attributed to the inherent sparsity of the frequency domain and the strong-value-focused nature of Softmax in vanilla attention. To address this, we enhance the vanilla attention mechanism by introducing an additional learnable matrix to the original attention matrix, followed by row-wise L1 normalization. Theoretical analysis~demonstrates that this enhanced attention mechanism improves both feature diversity and gradient flow. Extensive experiments demonstrate that FreEformer consistently outperforms state-of-the-art models on eighteen real-world benchmarks covering electricity, traffic, weather, healthcare and finance. Notably, the enhanced attention mechanism also consistently improves the performance of state-of-the-art Transformer-based forecasters.

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