Time series modeling is uniquely challenged by the presence of autocorrelation in both historical and label sequences. Current research predominantly focuses on handling autocorrelation within the historical sequence but often neglects its presence in the label sequence. Specifically, emerging forecast models mainly conform to the direct forecast (DF) paradigm, generating multi-step forecasts under the assumption of conditional independence within the label sequence. This assumption disregards the inherent autocorrelation in the label sequence, thereby limiting the performance of DF-based models. In response to this gap, we introduce the Frequency-enhanced Direct Forecast (FreDF), which bypasses the complexity of label autocorrelation by learning to forecast in the frequency domain. Our experiments demonstrate that FreDF substantially outperforms existing state-of-the-art methods including iTransformer and is compatible with a variety of forecast models.

翻译：时间序列建模面临独特挑战：历史序列与标签序列中均存在自相关现象。当前研究主要聚焦于处理历史序列中的自相关，却往往忽视标签序列中同样存在的自相关特性。具体而言，新兴预测模型大多遵循直接预测范式，在假设标签序列条件独立的前提下生成多步预测。该假设忽略了标签序列固有的自相关特性，从而限制了基于直接预测模型的表现。针对这一缺陷，我们提出频域增强直接预测方法（FreDF），通过在学习过程中将预测任务迁移至频域，绕开标签自相关带来的复杂性。实验结果表明，FreDF在性能上显著超越包括iTransformer在内的现有最优方法，且能与多种预测模型兼容适配。