---
title: "Five Ways to Fine-Tune Chronos-2, the Time Series Foundation Model"
url: https://daily.dev/posts/five-ways-to-fine-tune-chronos-2-the-time-series-foundation-model-9prvd9tlw
source_url: https://towardsdatascience.com/five-ways-to-fine-tune-chronos-2-the-time-series-foundation-model
type: article
source: "Towards Data Science"
published: 2026-06-04T16:55:05.729Z
updated: 2026-06-04T16:55:33.037Z
tags: ["python", "deep-learning", "time-series-forecasting", "lora"]
reading_time: 17
upvotes: 0
comments: 0
language: en
---

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# Five Ways to Fine-Tune Chronos-2, the Time Series Foundation Model

**[Towards Data Science](https://daily.dev/sources/tds)** · 17 min read · 0 upvotes · 0 comments

## Summary

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.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/five-ways-to-fine-tune-chronos-2-the-time-series-foundation-model>

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---

Tags: [#python](https://daily.dev/tags/python), [#deep-learning](https://daily.dev/tags/deep-learning), [#time-series-forecasting](https://daily.dev/tags/time-series-forecasting), [#lora](https://daily.dev/tags/lora)

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