利用非抽取小波包特征与Transformer模型进行时间序列预测
Leveraging Non-Decimated Wavelet Packet Features and Transformer Models for Time Series Forecasting
AI总结：
本文结合Daubechies小波特征与Transformer等预测模型，通过非抽取小波包变换提取特征，在多种方法上验证了其对一步及长期预测的改进效果。
摘要：
本文结合小波分析技术与机器学习方法进行单变量时间序列预测，主要贡献有三点。首先，我们考虑使用具有不同消失矩数量的Daubechies小波作为输入特征，应用于非时间序列和时间序列预测方法，通过在交叉验证阶段选择这些消失矩数量。其次，我们比较了使用非抽取小波变换和非抽取小波包变换来计算这些特征，后者提供了更大规模的潜在有用系数向量集合。小波系数使用典型金字塔算法的移位版本计算，以确保未来信息不会泄漏到这些输入中。第三，我们评估了这些小波特征在比以往研究更广泛的预测方法上的使用，包括时间序列和非时间序列模型，以及统计和基于深度学习的方法。后者包括最先进的基于Transformer的神经网络架构。我们的实验表明，在所有考察的非时间序列方法中，对于一步向前预测，用小波特征替代高阶滞后特征具有显著益处，而对于长期预测，将其作为时间序列深度学习模型的输入时具有适度益处。
英文摘要：
This article combines wavelet analysis techniques with machine learning methods for univariate time series forecasting, focusing on three main contributions. Firstly, we consider the use of Daubechies wavelets with different numbers of vanishing moments as input features to both non-temporal and temporal forecasting methods, by selecting these numbers during the cross-validation phase. Secondly, we compare the use of both the non-decimated wavelet transform and the non-decimated wavelet packet transform for computing these features, the latter providing a much larger set of potentially useful coefficient vectors. The wavelet coefficients are computed using a shifted version of the typical pyramidal algorithm to ensure no leakage of future information into these inputs. Thirdly, we evaluate the use of these wavelet features on a significantly wider set of forecasting methods than previous studies, including both temporal and non-temporal models, and both statistical and deep learning-based methods. The latter include state-of-the-art transformer-based neural network architectures. Our experiments suggest significant benefit in replacing higher-order lagged features with wavelet features across all examined non-temporal methods for one-step-forward forecasting, and modest benefit when used as inputs for temporal deep learning-based models for long-horizon forecasting.
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。
