pythonpapertime-seriesitransformerforecastingafter-work
Introducing iTransformer: Inverted Transformers Are Effective for Time Series Forecasting for after-work quants
Liu, Hu, Zhang, Wu, Wang, Ma and Long (ICLR 2024; arXiv:2310.06625) invert the Transformer: each variate’s full look-back is one token. Attention mixes assets; FFN (shared MLP) encodes that series; LayerNorm is per variate. No new attention gadget. Strong on high-dimensional ECL/Traffic/PEMS; inverting also lifts vanilla/Informer/Reformer. thuml/iTransformer. Plain-language intro. Not investment advice.