摘要：

多元时间序列的各子序列包含不同时间跨度的多尺度特征，现有时间序列预测模型不能有效地捕获多尺度特征以及评估其重要程度。提出一种基于多尺度时序特征融合与双注意力机制的多元时间序列预测网络FFANet，有效融合多尺度特征并关注其中重要部分。通过多尺度时序特征融合模块中并行的时序膨胀卷积层，使模型具有多种感受域，从而提取时序数据在不同尺度上的特征，并根据重要性对其进行自适应融合。利用双注意力模块对融合的时序特征进行重新标定，通过分配时序和通道注意力权重并加权至对应的时序特征，使FFANet聚焦对预测有重要贡献的特征。实验结果表明，相比AR、VARMLP、RNN-GRU、LSTNet-skip、TPA-LSTM、MTGNN和AttnAR时间序列预测模型，FFANet在Traffic、Solar Energy和Electricity数据集上的RRSE预测误差分别平均降低0.152 3、0.120 0、0.074 3、0.035 4、0.021 5、0.012 1、0.020 0。

关键词: 多元时间序列预测, 卷积神经网络, 多尺度特征, 特征融合, 注意力机制

Abstract:

Each subsequence of the Multivariate Time Series(MTS) contains multi-scale characteristics of different time spans, comprising information such as development process, direction, and trend. However, existing time series prediction models cannot effectively capture multi-scale features and evaluate their importance. In this study, a MTS prediction network, FFANet, is proposed based on multi-scale temporal feature fusion and a Dual-Attention Mechanism(DAM).FFANet effectively integrates multi-scale features and focuses on important parts.Utilizing the parallel temporal dilation convolution layer in the multi-scale temporal feature fusion module endows the model with multiple receptive domains to extract features of temporal data at different scales and adaptively fuse them based on their importance. Using a DAM to recalibrate the fused temporal features, FFANet focuses on features that make significant contributions to prediction by assigning temporal and channel attention weights and weighting them to the corresponding temporal features. The experimental results show that compared with AR, VARMLP, RNN-GRU, LSTNet-skip, TPA-LSTM, MTGNN, and AttnAR time series prediction models, FFANet achieves average reduction of 0.152 3、0.120 0、0.074 3、0.035 4、0.021 5、0.012 1、0.020 0 in RRSE prediction error on Traffic, Solar Energy, and Electricity datasets, respectively.

Key words: Multivariate Time Series(MTS) forecasting, Convolutional Neural Network(CNN), multi-scale feature, feature fusion, attention mechanism