Multi-resolution Time-Series Transformer for Long-term Forecasting

• URL: http://arxiv.org/abs/2311.04147v2
• Date: Fri, 22 Mar 2024 15:37:38 GMT
• Title: Multi-resolution Time-Series Transformer for Long-term Forecasting
• Authors: Yitian Zhang, Liheng Ma, Soumyasundar Pal, Yingxue Zhang, Mark Coates,
• Abstract summary: We propose a novel framework, Multi-resolution Time-Series Transformer (MTST), for simultaneous modeling of diverse temporal patterns at different resolutions. In contrast to many existing time-series transformers, we employ relative positional encoding, which is better suited for extracting periodic components at different scales.
• Score: 24.47302799009906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The performance of transformers for time-series forecasting has improved significantly. Recent architectures learn complex temporal patterns by segmenting a time-series into patches and using the patches as tokens. The patch size controls the ability of transformers to learn the temporal patterns at different frequencies: shorter patches are effective for learning localized, high-frequency patterns, whereas mining long-term seasonalities and trends requires longer patches. Inspired by this observation, we propose a novel framework, Multi-resolution Time-Series Transformer (MTST), which consists of a multi-branch architecture for simultaneous modeling of diverse temporal patterns at different resolutions. In contrast to many existing time-series transformers, we employ relative positional encoding, which is better suited for extracting periodic components at different scales. Extensive experiments on several real-world datasets demonstrate the effectiveness of MTST in comparison to state-of-the-art forecasting techniques.

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