Forking-Sequences — Part I: Statistically and Computationally Efficient Multi-Horizon Forecasting

Based on: Potosnak, W., Wolff, M., Cao, M., Ma, R., Konstantinova, T., Efimov, D., Mahoney, M.W., Oreshkin, B., & Olivares, K.G. "Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility." Transactions on Machine Learning Research, 2026. (Disclaimer: Code implementation not used in the paper; not affiliated with Amazon — provided as a reference for forking-sequences and forecast ensembling) TL;DR One architectural swap, no new parameters. Instead of encoding one forecast window at a time in the window-sampling training paradigm, forking-sequences encodes and decodes the entire series across all forecast creation dates in a single forward pass. Encoder-Agnostic. Validated across six encoder types — MLP, RNN, LSTM, CNN, Transformer, and State Space. Better gradients, faster training. Loss is pooled across every forecast creation date, cutting gradient variance at a linear rate and fixing vanishing gradients in RNNs/LSTMs — sCRPS improved up to 49.3% for LSTM encoders. Order-of-magnitude cheaper inference. Reusing encoder outputs across T forecast dates cuts cross-validation complexity by a factor of T for every encoder type — e.g. for LSTMs, RNNs, and CNNs, O(T²) drops to O(T). Multi-horizon forecasting has become the default approach across energy, finance, supply chain, and healthcare applications, because it gives planners visibility into […]