WEITS：一种用于可解释时间序列预测的小波增强残差框架
WEITS: A Wavelet-enhanced residual framework for interpretable time series forecasting
AI总结：
针对深度时序预测可解释性不足且优化复杂的问题，提出频率感知框架 WEITS，利用多级小波分解与前后向残差架构兼顾表示能力、统计可解释性和计算效率。
摘要：
时间序列（TS）预测近年来已成为一个空前热门的问题，在科学和商业领域都有着无处不在的应用。多种方法已被引入时间序列分析，包括统计方法和深度神经网络。尽管神经网络方法已展现出比统计方法更强的表示能力，但它们难以提供充分的可解释性，并且可能过于复杂而难以优化。本文提出 WEITS，这是一个高度可解释且计算高效的频率感知深度学习框架。通过多级小波分解，WEITS 新颖地将频率分析融入一个高度深度学习框架。结合前后向残差架构，它同时具备高表示能力和统计可解释性。在真实世界数据集上的大量实验证明了我们模型具有竞争力的性能，以及其高计算效率的额外优势。此外，WEITS 提供了一个通用框架，始终能够与最先进的时间序列预测方法无缝集成。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
