Forking-Sequences — Part II: Multi-Horizon Forecast Ensembling with Reduced Volatility

This CMU ML blog post (Part II of a series) presents forking-sequences as a mechanism for multi-horizon forecast ensembling with reduced volatility. Key contributions include two new forecast volatility metrics — scaled Forecast Percentage Change (sFPC), usable in real-time without ground truth, and scaled Excess Volatility (sEV), which rewards accuracy-improving revisions and only penalizes harmful ones. Empirically, applying exponential-smoothing ensembling (α=0.9) to forking-sequences models reduces sEV by 10–13% across RNN, LSTM, CNN, Transformer, and StateSpace architectures with less than 0.1% accuracy degradation. The technique also works zero-shot on pretrained Time Series Foundation Models (Chronos-2, Toto 2.0, TimesFM, PatchTST, N-BEATS), achieving ~10% volatility reduction at negligible accuracy cost.

Source: https://blog.ml.cmu.edu/2026/08/10/forking-sequences-part-ii-multi-horizon-forecast-ensembling-with-reduced-volatility. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.