Five Ways to Fine-Tune Chronos-2, the Time Series Foundation Model
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A hands-on guide to fine-tuning Chronos-2, Amazon's 120M-parameter time series foundation model, using LoRA (Low-Rank Adaptation). Five scenarios are covered using a building electricity-demand case study: single-asset adaptation, portfolio fine-tuning across a fleet, covariate-informed fine-tuning with known future signals, combining portfolio and covariates, and held-out transfer to unseen assets. Results show that target-only fine-tuning yields modest gains (e.g., 8.3% → 7.6% WAPE), while covariate-informed fine-tuning delivers much larger improvements (e.g., 4.0% → 2.8% WAPE, a 30.7% reduction). The portfolio + covariates approach achieves a 66.8% relative WAPE reduction. Code examples use the Hugging Face `peft` library and the Chronos-2 `.fit()` API, with early stopping and checkpoint selection.
