How Much Can Time-related Features Enhance Time Series Forecasting?

• URL: http://arxiv.org/abs/2412.01557v1
• Date: Mon, 02 Dec 2024 14:45:26 GMT
• Title: How Much Can Time-related Features Enhance Time Series Forecasting?
• Authors: Chaolv Zeng, Yuan Tian, Guanjie Zheng, Yunjun Gao,
• Abstract summary: We introduce a module designed to encode time-related features, Time Stamp Forecaster (TimeSter)<n>TimeSter significantly improves the performance of a single linear projector, reducing MSE by an average of 23% on benchmark datasets such as Electricity and Traffic.
• Score: 27.030553080458716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in long-term time series forecasting (LTSF) have primarily focused on capturing cross-time and cross-variate (channel) dependencies within historical data. However, a critical aspect often overlooked by many existing methods is the explicit incorporation of \textbf{time-related features} (e.g., season, month, day of the week, hour, minute), which are essential components of time series data. The absence of this explicit time-related encoding limits the ability of current models to capture cyclical or seasonal trends and long-term dependencies, especially with limited historical input. To address this gap, we introduce a simple yet highly efficient module designed to encode time-related features, Time Stamp Forecaster (TimeSter), thereby enhancing the backbone's forecasting performance. By integrating TimeSter with a linear backbone, our model, TimeLinear, significantly improves the performance of a single linear projector, reducing MSE by an average of 23\% on benchmark datasets such as Electricity and Traffic. Notably, TimeLinear achieves these gains while maintaining exceptional computational efficiency, delivering results that are on par with or exceed state-of-the-art models, despite using a fraction of the parameters.

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