标题： 通过对比课程学习实现时间序列基础模型用于建筑能源预测 显示英文标题

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

摘要： 时间序列预测的进步正在推动从传统机器学习模型向基于通用知识训练的基础模型（FMs）的转变。 然而，现有的FMs在能源领域中的表现仍然不佳，例如建筑能耗预测（BEF）。 本文研究了将FM适应于BEF任务的方法。 我们从FM和数据两个角度展示了直接微调FM的不足之处。 为了克服这些限制，我们提出了一种基于\textit{对比课程学习}的训练方法。 我们的方法在TSFM适应的背景下优化了训练数据的顺序。 实验表明，与现有FMs相比，我们的方法可以将零次/少次学习性能提高14.6%。 我们的代码和新的TSFM将在<Anonymous Github Repo>上提供。 显示英文摘要

摘要： 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 \textit{contrastive 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>.