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.

翻译：近年来，长期时间序列预测（LTSF）的研究进展主要集中于捕捉历史数据中的跨时间和跨变量（通道）依赖性。然而，许多现有方法常常忽视一个关键方面，即对**时间相关特征**（例如季节、月份、星期几、小时、分钟）的显式融入，而这些特征是时间序列数据的重要组成部分。缺乏这种显式的时间相关编码限制了当前模型捕捉周期性或季节性趋势以及长期依赖关系的能力，尤其是在历史输入有限的情况下。为弥补这一不足，我们引入了一个简单而高效的模块，旨在编码时间相关特征，即时间戳预测器（TimeSter），从而提升主干网络的预测性能。通过将TimeSter与线性主干网络集成，我们的模型TimeLinear显著提升了单一线性投影器的性能，在Electricity和Traffic等基准数据集上平均降低了23%的均方误差。值得注意的是，TimeLinear在取得这些性能提升的同时，保持了卓越的计算效率，其性能与最先进的模型相当甚至更优，而参数量却仅为后者的一小部分。