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

DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction