加入时间特征编码，让线性模型也能精准预测长期时序。
How Much Can Time-related Features Enhance Time Series Forecasting?
- 设计时间戳编码模块，显式注入季节、星期、小时等时间信息。
- 在电力与交通数据集上，均方误差降低23%，优于主流模型。
- 参数极少，计算高效，适合资源受限场景部署。
近期长时序预测（LTSF）研究主要关注历史数据中的跨时间与跨变量依赖关系，但多数方法忽略了显式编码时间相关特征（如季节、月份、星期几、小时、分钟）这一关键要素。这种缺失限制了模型对周期性趋势和长期依赖的捕捉能力，尤其在历史输入有限时更为明显。为此，我们提出一个简单高效的模块——时间戳预测器（TimeSter），用于编码时间相关特征。将其与线性主干网络结合，构建出TimeLinear模型。该模型在Electricity和Traffic等基准数据集上，相比单一线性投影器，平均将均方误差（MSE）降低23%。值得注意的是，尽管参数量极小，其性能仍可媲美甚至超越当前最先进的模型，展现出卓越的计算效率。
原文摘要 · Abstract (English)
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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