# Enabling Time-series Foundation Model for Building Energy Forecasting via Contrastive Curriculum Learning

arXiv: https://arxiv.org/abs/2412.17285 · HTML: https://mlsanity.com/paper/2412.17285/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 2 of 2)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Chronos: Learning the Language of Time Series](https://mlsanity.com/paper/2403.07815/) (2024)

## Built on by (most cited first, 1 of 1)

- [Can Time-Series Foundation Models Perform Building Energy Management Tasks?](https://mlsanity.com/paper/2506.11250/) (2025)

## Abstract (arXiv)

Advances in time-series forecasting are driving a shift from conventional machine learning models to foundation models (FMs) that are trained with generalized knowledge. However, existing FMs still perform poorly in the energy fields, such as building energy forecasting (BEF). This paper studies the adaptation of FM to BEF tasks. We demonstrate the shortcomings of fine-tuning FM straightforwardly from both the perspectives of FM and the data. To overcome these limitations, we propose a new textitcontrastive curriculum learning-based training method. Our method optimizes the ordering of training data in the context of TSFM adaptation. Experiments show that our method can improve the zero/few-shot performance by 14.6% compared to the existing FMs. Our code and new TSFM will be available at <Anonymous Github Repo>.