Prophet as a Reproducible Forecasting Framework: A Methodological Guide for Business and Financial Analytics

TL;DR

This paper evaluates Prophet, an open-source forecasting tool, highlighting its role in enhancing reproducibility, interpretability, and standardized workflows in business and financial analytics, through comparative analysis and practical Python examples.

Contribution

It provides a comprehensive assessment of Prophet's reproducibility benefits and demonstrates its integration into transparent forecasting workflows without proposing new algorithms.

Findings

Prophet offers improved reproducibility over traditional methods.

It balances interpretability with predictive flexibility.

Prophet facilitates efficient, transparent forecasting workflows.

Abstract

Reproducibility remains a persistent challenge in forecasting research and practice, particularly in business and financial analytics, where forecasts inform high-stakes decisions. Traditional forecasting methods, while theoretically interpretable, often require extensive manual tuning and are difficult to replicate in proprietary environments. Machine learning approaches offer predictive flexibility but introduce challenges related to interpretability, stochastic training procedures, and cross-environment reproducibility. This paper examines Prophet, an open-source forecasting framework developed by Meta, as a reproducibility-enabling solution that balances interpretability, standardized workflows, and accessibility. Rather than proposing a new algorithm, this study evaluates how Prophet's additive structure, open-source implementation, and standardized workflow contribute to transparent and replicable forecasting practice. Using publicly available financial and retail datasets, we compare the performance and interpretability of Prophet with multiple ARIMA specifications (auto-selected, manually specified, and seasonal variants) and Random Forest, under a controlled and fully documented experimental design. This multi-model comparison provides a robust assessment of Prophet's relative performance and reproducibility advantages. Through concrete Python examples, we demonstrate how Prophet facilitates efficient forecasting workflows and integration with analytical pipelines. The study positions Prophet within the broader context of reproducible research. It highlights Prophet's role as a methodological building block that supports verification, auditability, and methodological rigor. This work provides researchers and practitioners with a practical reference framework for reproducible forecasting in Python-based research workflows.