AnchorMix: Distilling Heterogeneous Forecasters into a Single Routed Time-Series Model

Heterogeneous time-series forecasters capture complementary inductive biases, but exploiting them through runtime ensembles multiplies latency, memory, and deployment dependencies. We introduce AnchorMix, a two-stage framework that converts this diversity into one student checkpoint. Stage 1 learns a routed temporal-query anchor from labels and cached Chronos-2 forecasts. Stage 2 warm-starts the same architecture and trains it against ground truth plus a convex target mixing the frozen anchor with an architecture-heterogeneous teacher. The anchor preserves established student behavior while the external teacher supplies complementary corrections; every teacher is removed after training. Across five benchmarks, four horizons, and three seeds, the single student improves 29 of 40 MSE/MAE cells relative to the selected external teachers and records lower values in 39 of 40 cells against four recent published comparison tables. A full-array diagnostic finds a positive convex-combination advantage on all 20 dataset–horizon tasks, and target-composition ablations on ETTh2 and ETTm2 identify the roles of anchor preservation and heterogeneous correction. The deployed model contains 4.06–5.36M parameters and averages 1.19 ms per batch of 32. On matched RTX 5090 profiles, replacing the corresponding runtime ensemble yields up to a 181.7x speedup and 80.7% lower peak allocated memory. AnchorMix therefore turns heterogeneous forecast diversity from a deployment burden into a training-time resource for one compact forecaster.