# Benchmarking Time Series Forecasting Models: From Statistical Techniques to Foundation Models in Real-World Applications

arXiv: https://arxiv.org/abs/2502.03395 · HTML: https://mlsanity.com/paper/2502.03395/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2025.

## Abstract (arXiv)

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.