Abstract
Time series forecasting is essential for operational intelligence in the hospitality industry, and particularly challenging in large-scale, distributed systems. This study evaluates the performance of statistical, machine learning (ML), deep learning, and foundation models in forecasting hourly sales over a 14-day horizon using real-world data from a network of thousands of restaurants across Germany. The forecasting solution includes features such as weather conditions, calendar events, and time-of-day patterns. Results demonstrate the strong performance of ML-based meta-models and highlight the emerging potential of foundation models like Chronos and TimesFM, which deliver competitive performance with minimal feature engineering, leveraging only the pre-trained model (zero-shot inference). Additionally, a hybrid PySpark-Pandas approach proves to be a robust solution for achieving horizontal scalability in large-scale deployments.
Abstract (translated)
时间序列预测对于酒店行业的运营智能至关重要，而在大规模分布式系统中实现这一点尤其具有挑战性。本研究评估了统计方法、机器学习（ML）、深度学习以及基础模型在使用德国数千家餐厅的真实世界数据来预测14天内每小时销售额方面的表现。该预测解决方案包括天气条件、日历事件和时间段模式等特征。 实验结果表明，基于机器学习的元模型表现出色，并强调了Chronos和TimesFM等基础模型的新兴潜力，这些模型在无需复杂特征工程的情况下仅通过预训练模型（零样本推理）即可提供具有竞争力的表现。此外，混合使用PySpark和Pandas的方法被证明是实现大规模部署横向扩展的一种稳健解决方案。
URL
https://arxiv.org/abs/2502.03395
