pythonpapertime-seriestimesnetforecastingafter-work
Introducing TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis for after-work quants
Wu, Hu, Liu, Zhou, Wang and Long (ICLR 2023; arXiv:2210.02186) argue 1D series hide intraperiod vs interperiod variation. FFT picks top-k periods, reshape each into a 2D tensor (columns = inside a period, rows = same phase across periods), run a shared Inception block, softmax-amplitude mix. SOTA-ish across forecasting, imputation, UEA classification, anomaly detection. thuml/TimesNet. Plain-language intro. Not investment advice.