Multivariate time series long-term prediction, which aims to predict the change of data in a long time, can provide references for decision-making. Although transformer-based models have made progress in this field, they usually do not make full use of three features of multivariate time series: global information, local information, and variables correlation. To effectively mine the above three features and establish a high-precision prediction model, we propose a double sampling transformer (DSformer), which consists of the double sampling (DS) block and the temporal variable attention (TVA) block. Firstly, the DS block employs down sampling and piecewise sampling to transform the original series into feature vectors that focus on global information and local information respectively. Then, TVA block uses temporal attention and variable attention to mine these feature vectors from different dimensions and extract key information. Finally, based on a parallel structure, DSformer uses multiple TVA blocks to mine and integrate different features obtained from DS blocks respectively. The integrated feature information is passed to the generative decoder based on a multi-layer perceptron to realize multivariate time series long-term prediction. Experimental results on nine real-world datasets show that DSformer can outperform eight existing baselines.

翻译：多变量时间序列长期预测旨在预测数据在长时间内的变化，可为决策提供参考。尽管基于Transformer的模型在此领域取得了进展，但它们通常未能充分利用多变量时间序列的三个特征：全局信息、局部信息和变量相关性。为了有效挖掘上述三个特征并建立高精度预测模型，我们提出了一种双重采样Transformer（DSformer），它由双重采样（DS）块和时间变量注意力（TVA）块组成。首先，DS块采用下采样和分段采样将原始序列转换为分别关注全局信息和局部信息的特征向量。然后，TVA块利用时间注意力和变量注意力从不同维度挖掘这些特征向量并提取关键信息。最后，基于并行结构，DSformer使用多个TVA块分别挖掘和整合从DS块获得的不同特征。整合后的特征信息传递给基于多层感知机的生成式解码器，以实现多变量时间序列的长期预测。在九个真实世界数据集上的实验结果表明，DSformer的性能优于八个现有基线模型。