DSformer: A Double Sampling Transformer for Multivariate Time Series
  Long-term Prediction
Chengqing Yu Fei Wang Zezhi Shao Tao Sun Lin Wu Yongjun Xu
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.