Time series (TS) forecasting has been an unprecedentedly popular problem in recent years, with ubiquitous applications in both scientific and business fields. Various approaches have been introduced to time series analysis, including both statistical approaches and deep neural networks. Although neural network approaches have illustrated stronger ability of representation than statistical methods, they struggle to provide sufficient interpretablility, and can be too complicated to optimize. In this paper, we present WEITS, a frequency-aware deep learning framework that is highly interpretable and computationally efficient. Through multi-level wavelet decomposition, WEITS novelly infuses frequency analysis into a highly deep learning framework. Combined with a forward-backward residual architecture, it enjoys both high representation capability and statistical interpretability. Extensive experiments on real-world datasets have demonstrated competitive performance of our model, along with its additional advantage of high computation efficiency. Furthermore, WEITS provides a general framework that can always seamlessly integrate with state-of-the-art approaches for time series forecast.

翻译：时间序列预测近年来在科学与商业领域具有广泛应用，成为前所未有的热门研究问题。针对时间序列分析，研究者已提出多种方法，涵盖统计方法与深度神经网络。尽管神经网络方法在表示能力上优于统计方法，但其可解释性不足且优化过程过于复杂。本文提出WEITS——一种具有高可解释性与计算效率的频率感知深度学习框架。通过多级小波分解，WEITS创新地将频率分析融入深度学习框架中，并结合前向-后向残差架构，同时具备强表示能力与统计可解释性。在真实数据集上的大量实验表明，该模型不仅具有竞争性性能，还具备高计算效率的额外优势。此外，WEITS提供通用框架，可无缝集成最新时间序列预测方法。