pythonpapertime-seriesdeeparprobabilisticafter-work
Introducing DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks for after-work quants
Salinas, Flunkert and Gasthaus (Amazon, 2017; IJF 2020; arXiv:1704.04110) train one autoregressive LSTM on thousands of related series. The net emits likelihood parameters (Gaussian or negative binomial); ancestral sampling yields joint predictive paths and consistent quantiles. Scale handling + velocity-weighted sampling for power-law magnitudes. Plain-language intro. Not investment advice.