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

Forking-sequences is a training and inference paradigm for multi-horizon time series forecasting that encodes the entire series across all forecast creation dates (FCDs) in a single forward pass, rather than encoding one window at a time as in standard window-sampling. The approach is encoder-agnostic, validated across MLP, RNN, LSTM, CNN, Transformer, and State Space models. Theoretically, it reduces gradient variance at O(1/T) and improves SNR linearly with T, directly mitigating vanishing gradients in recurrent architectures. Empirically, LSTM encoders saw up to 49.3% sCRPS improvement, RNNs 46.2%, and CNNs 28.6% over window-sampling on M1/M3/M4/Tourism benchmarks. Computationally, reusing encoder outputs across FCDs drops cross-validation complexity from O(T²) to O(T) for LSTMs, RNNs, and CNNs, and from O(T³) to O(T²) for Transformers. Part II will cover forecast volatility reduction through ensembling.

Source: https://blog.ml.cmu.edu/2026/08/10/forking-sequences-part-i-statistically-and-computationally-efficient-multi-horizon-forecasting. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.