We propose an efficient design of Transformer-based models for multivariatetime series forecasting and self-supervised representation learning. It isbased on two key components: (i) segmentation of time series intosubseries-level patches which are served as input tokens to Transformer; (ii)channel-independence where each channel contains a single univariate timeseries that shares the same embedding and Transformer weights across all theseries. Patching design naturally has three-fold benefit: local semanticinformation is retained in the embedding; computation and memory usage of theattention maps are quadratically reduced given the same look-back window; andthe model can attend longer history. Our channel-independent patch time seriesTransformer (PatchTST) can improve the long-term forecasting accuracysignificantly when compared with that of SOTA Transformer-based models. We alsoapply our model to self-supervised pre-training tasks and attain excellentfine-tuning performance, which outperforms supervised training on largedatasets. Transferring of masked pre-trained representation on one dataset toothers also produces SOTA forecasting accuracy. Code is available at:https://github.com/yuqinie98/PatchTST.