关键词: 时间序列, 多变量时间序列预测, 深度可分离卷积, 自注意力机制, 局部与全局依赖

Abstract: Concerning the insufficient integration of local and global dependencies in existing time series models, a method for multivariate time series prediction namely PatchLG (Patch-integrated Local-Global correlation method) was proposed, which can better integrate both local and global correlations. The proposed method was based on three key components: 1) segmenting the time series into multiple patches, preserving the locality of the time series while making it easier for the model to capture global dependencies; 2) utilizing depthwise separable convolution and self-attention mechanism to model local and global correlations; 3) decomposing the time series into trend and seasonal components, and combining the predictions of these two components to obtain the final result. Experimental results on seven benchmark datasets demonstrate that PatchLG achieves an average improvement of 3.0% and 2.9% in Mean-Square Error (MSE) and Mean Absolute Error (MAE), respectively, compared to the optimal baseline method PatchTST, and possesses low actual running time and memory usage, validating the effectiveness of PatchLG in time series forecasting.

Key words: time series, multivariate time series prediction, depth-separable convolution, self-attention mechanism, local and global dependence