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

• URL: http://arxiv.org/abs/2305.14649v2
• Date: Sat, 28 Oct 2023 05:16:59 GMT
• Title: A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting
• Authors: Yushu Chen, Shengzhuo Liu, Jinzhe Yang, Hao Jing, Wenlai Zhao, and Guangwen Yang
• Abstract summary: This paper introduces the Joint Time-Frequency Domain Transformer (JTFT) JTFT combines time and frequency domain representations to make predictions. Experimental results on six real-world datasets demonstrate that JTFT outperforms state-of-the-art baselines in predictive performance.
• Score: 7.501660339993144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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