metadata
license: mit
library_name: xgboost
tags:
- time-series
- time-series-forecasting
- gift-eval
- xgboost
- foundation-models
TimeRouter
Trained XGBoost router weights for TimeRouter, the GIFT-EVAL submission that routes among 4 frozen time-series foundation models (Chronos-2, FlowState, PatchTST-FM, Sundial) with a margin/diversity gate and a CV-inverse-weighted fallback.
LB MASE = 0.6746 on the full 97-config GIFT-EVAL test suite.
Files
| File | Description |
|---|---|
seed42.json … seed46.json |
5-seed XGBoost OvA ensemble. 305-dim features, 400 trees × depth 8 × lr 0.05 × subsample 0.8, random_state ∈ {42..46}, tree_method="hist". |
pool_metadata.json |
Pool config ({chronos, flowstate, patchtst_fm, sundial}), 305-feature column order, and gate thresholds (tau_m, tau_d) = (0.15, 0.02). |
Usage
These are the checkpoints loaded by gift_eval/run_eval.py --ckpt-dir <this folder> in the
TimeRouter repository; see its README for the
full two-environment setup and run instructions. Requires xgboost >= 2.x.
from huggingface_hub import snapshot_download
ckpt_dir = snapshot_download("nkh/timerouter-v1")
# then: python gift_eval/run_eval.py --ckpt-dir $ckpt_dir ...
Citation
If you use these checkpoints, please cite the TimeRouter repository (UConn Data Science and Intelligent System (DSIS) Research Lab).