  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.
