TFDNet: Time-Frequency Enhanced Decomposed Network for Long-term Time Series Forecasting

• URL: http://arxiv.org/abs/2308.13386v1
• Date: Fri, 25 Aug 2023 14:01:43 GMT
• Title: TFDNet: Time-Frequency Enhanced Decomposed Network for Long-term Time Series Forecasting
• Authors: Yuxiao Luo, Ziyu Lyu, Xingyu Huang
• Abstract summary: Long-term time series forecasting is a vital task and has a wide range of real applications. Recent methods focus on capturing the underlying patterns from one single domain. We propose a Time-Frequency Enhanced Decomposed Network (TFDNet) to capture both the long-term underlying patterns and temporal periodicity from the time-frequency domain.
• Score: 2.6361094144982005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-term time series forecasting is a vital task and has a wide range of real applications. Recent methods focus on capturing the underlying patterns from one single domain (e.g. the time domain or the frequency domain), and have not taken a holistic view to process long-term time series from the time-frequency domains. In this paper, we propose a Time-Frequency Enhanced Decomposed Network (TFDNet) to capture both the long-term underlying patterns and temporal periodicity from the time-frequency domain. In TFDNet, we devise a multi-scale time-frequency enhanced encoder backbone and develop two separate trend and seasonal time-frequency blocks to capture the distinct patterns within the decomposed trend and seasonal components in multi-resolutions. Diverse kernel learning strategies of the kernel operations in time-frequency blocks have been explored, by investigating and incorporating the potential different channel-wise correlation patterns of multivariate time series. Experimental evaluation of eight datasets from five benchmark domains demonstrated that TFDNet is superior to state-of-the-art approaches in both effectiveness and efficiency.

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