pythonpapertime-seriesinformertransformerafter-work
Introducing Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting for after-work quants
Zhou, Zhang, Peng, Zhang, Li, Xiong and Zhang (AAAI 2021; arXiv:2012.07436) fix three Transformer LSTF bottlenecks: quadratic attention (ProbSparse O(L log L)), stacked-layer memory (self-attention distilling), and slow step-by-step decode (one-shot generative decoder). ETT oil temperature, ECL, Weather. Plain-language intro. Not investment advice.