Many real-world applications require the prediction of long sequencetime-series, such as electricity consumption planning. Long sequencetime-series forecasting (LSTF) demands a high prediction capacity of the model,which is the ability to capture precise long-range dependency coupling betweenoutput and input efficiently. Recent studies have shown the potential ofTransformer to increase the prediction capacity. However, there are severalsevere issues with Transformer that prevent it from being directly applicableto LSTF, including quadratic time complexity, high memory usage, and inherentlimitation of the encoder-decoder architecture. To address these issues, wedesign an efficient transformer-based model for LSTF, named Informer, withthree distinctive characteristics: (i) a $ProbSparse$ self-attention mechanism,which achieves $O(L \log L)$ in time complexity and memory usage, and hascomparable performance on sequences' dependency alignment. (ii) theself-attention distilling highlights dominating attention by halving cascadinglayer input, and efficiently handles extreme long input sequences. (iii) thegenerative style decoder, while conceptually simple, predicts the longtime-series sequences at one forward operation rather than a step-by-step way,which drastically improves the inference speed of long-sequence predictions.Extensive experiments on four large-scale datasets demonstrate that Informersignificantly outperforms existing methods and provides a new solution to theLSTF problem.