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arXiv · 2305.14649

A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting

Yushu Chen·Shengzhuo Liu·Jinzhe Yang·Hao Jing·Wenlai Zhao·Guangwen Yang
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Abstract

In order to enhance the performance of Transformer models for long-term multivariate forecasting while minimizing computational demands, this paper introduces the Joint Time-Frequency Domain Transformer (JTFT). JTFT combines time and frequency domain representations to make predictions. The frequency domain representation efficiently extracts multi-scale dependencies while maintaining sparsity by utilizing a small number of learnable frequencies. Simultaneously, the time domain (TD) representation is derived from a fixed number of the most recent data points, strengthening the modeling of local relationships and mitigating the effects of non-stationarity. Importantly, the length of the representation remains independent of the input sequence length, enabling JTFT to achieve linear computational complexity. Furthermore, a low-rank attention layer is proposed to efficiently capture cross-dimensional dependencies, thus preventing performance degradation resulting from the entanglement of temporal and channel-wise modeling. Experimental results on six real-world datasets demonstrate that JTFT outperforms state-of-the-art baselines in predictive performance.

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Yushu Chen, Shengzhuo Liu, Jinzhe Yang, Hao Jing, Wenlai Zhao, Guangwen Yang. 2023-05-24. A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting. https://arxiv.org/abs/2305.14649

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