Unlocking the potential of deep learning in Peak-Hour Series Forecasting (PHSF) remains a critical yet underexplored task in various domains. While state-of-the-art deep learning models excel in regular Time Series Forecasting (TSF), they struggle to achieve comparable results in PHSF. This can be attributed to the challenges posed by the high degree of non-stationarity in peak-hour series, which makes direct forecasting more difficult than standard TSF. Additionally, manually extracting the maximum value from regular forecasting results leads to suboptimal performance due to models minimizing the mean deficit. To address these issues, this paper presents Seq2Peak, a novel framework designed specifically for PHSF tasks, bridging the performance gap observed in TSF models. Seq2Peak offers two key components: the CyclicNorm pipeline to mitigate the non-stationarity issue and a simple yet effective trainable-parameter-free peak-hour decoder with a hybrid loss function that utilizes both the original series and peak-hour series as supervised signals. Extensive experimentation on publicly available time series datasets demonstrates the effectiveness of the proposed framework, yielding a remarkable average relative improvement of 37.7% across four real-world datasets for both transformer- and non-transformer-based TSF models.

翻译：解锁深度学习在高峰时段序列预测（PHSF）中的潜力仍是各领域中一个关键但尚未充分探索的任务。尽管最先进的深度学习模型在常规时间序列预测（TSF）中表现出色，但在PHSF中却难以取得同样优异的结果。这归因于高峰时段序列的高度非平稳性带来的挑战，使得直接预测比标准TSF更为困难。此外，由于模型倾向于最小化均值偏差，从常规预测结果中手动提取最大值会导致性能次优。为解决这些问题，本文提出Seq2Peak，一个专门为PHSF任务设计的新颖框架，旨在弥合TSF模型在性能上的差距。Seq2Peak包含两个关键组件：用于缓解非平稳性问题的CyclicNorm流水线，以及一个简单且无需可训练参数的峰时解码器，该解码器采用混合损失函数，将原始序列和峰时序列同时作为监督信号。在公开时间序列数据集上的广泛实验证明了所提框架的有效性，在四个真实世界数据集上，基于Transformer和非Transformer的TSF模型均实现了平均相对改进37.7%的显著效果。