对比多种模型在千家餐厅小时级销售预测中的表现，发现基础模型零样本推理效果出色。
Benchmarking Time Series Forecasting Models: From Statistical Techniques to Foundation Models in Real-World Applications
- 用机器学习元模型和基础模型进行小时销售预测，减少特征工程依赖
- Chronos和TimesFM等基础模型在14天预测上表现接近甚至超越传统方法
- 适合需要大规模部署且资源有限的工业级时序预测场景
时间序列预测对酒店业运营智能至关重要，尤其在大规模分布式系统中更具挑战性。本研究基于德国数千家餐厅的真实数据，评估了统计方法、机器学习（ML）、深度学习及基础模型在14天时长的小时级销售预测中的性能。预测方案包含天气、日历事件和时段模式等特征。结果表明，基于机器学习的元模型表现优异，同时以Chronos和TimesFM为代表的基础模型展现出强劲潜力，仅需预训练模型即可实现零样本推理，在无需复杂特征工程的情况下达到竞争性表现。此外，结合PySpark与Pandas的混合方法在实现大规模横向扩展方面表现出显著鲁棒性。
原文摘要 · Abstract (English)
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.
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