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