基于时域频域混合特征的多变量时序预测模型

doi:10.3778/j.issn.1673-9418.2410089

摘要/Abstract

摘要： 目前多变量时间序列预测方法主要是将时间序列转换为频域表示来提取特征信息，然而频域表示下序列会损失部分时域信息造成精度损失，且传统注意力机制会产生平方级时间复杂度。针对上述问题，提出了一种基于时域频域混合特征的多变量时序预测模型（TFMformer）。模型采用多尺度切分操作，以多尺度混合的视角分解出更准确语义，增强模型捕获序列综合语义信息的能力并通过切片操作减少模型输入token数来降低时间复杂度。提出时域频域混合特征增强模块，将时域和频域特征进行融合与交互，从而提升整体特征表征能力。同时提出在频域注意力的基础上引入时域特征信息，提升频域空间对时域信息感知能力，使得模型能够更精准地聚焦于有意义的特征组合，减少因时域信息缺失造成的预测偏差。TFMformer模型在6个基准数据集上进行了实验，预测结果与现有的先进方法相比，均方误差和平均绝对误差分别平均下降了3.8%和2.8%，其中平均绝对误差最高下降了11.2%，证明了该模型的有效性。

关键词: 多变量时间序列预测, Transformer, 序列分解, 频域注意力机制, 深度学习

Abstract: Currently, multivariate time series prediction methods mainly transform time series into frequency domain repre-sentations for feature extraction. But this leads to the loss of some time domain information and accuracy, and the traditional attention mechanism brings quadratic time complexity. To address these issues, a multivariate time series prediction model (TFMformer) based on hybrid time-frequency domain features is proposed. TFMformer uses a multi-scale segmentation operation to decompose more accurate semantics from a multi-scale perspective, enhancing the model??s ability to capture comprehensive semantic information of the series. It reduces the number of input tokens through slicing to lower time complexity. A hybrid time-frequency domain feature enhancement module is introduced to make time domain and frequency domain features fuse and interact, improving overall feature representation. Additionally, time domain feature information is incorporated into frequency domain attention to enhance the frequency domain??s perception of time domain information, enabling the model to focus more precisely on meaningful feature combinations and reducing prediction bias due to the lack of time domain information. TFMformer is tested on six benchmark datasets. Compared with existing advanced methods, the mean squared error and mean absolute error of the prediction results are decreased by an average of 3.8% and 2.8% respectively, and the maximum reduction in mean absolute error reaches 11.2%, which proves the effectiveness of model.

Key words: multivariate time series prediction, Transformer, sequence decomposition, frequency domain attention mechanism, deep learning

闵锋, 刘宇卓, 刘煜晖, 刘彪. 基于时域频域混合特征的多变量时序预测模型[J]. 计算机科学与探索, 2025, 19(8): 2099-2109.

MIN Feng, LIU Yuzhuo, LIU Yuhui, LIU Biao. Multivariate Time Series Prediction Model Based on Mixed Features of Time Domain and Frequency Domain[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(8): 2099-2109.

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