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

Sidney Shapiro, Burhanuddin Panvelwala

TL;DR

The paper evaluates Prophet as a reproducibility-enabling forecasting framework for business and financial analytics, emphasizing open workflows, explicit documentation, and controlled experiments rather than new algorithmic development. It compares Prophet against ARIMA variants and Random Forest on Tesla stock prices and store-demand data to illustrate how specification variability affects results and how Prophet can reduce such variability through standardized, script-based processes. Empirical results show Prophet offers strong reproducibility and interpretability, with context-dependent point accuracy relative to traditional benchmarks; it provides consistently calibrated uncertainty intervals, and its component-wise decomposition supports auditability and communication. Overall, Prophet is presented as a robust methodological baseline for reproducible forecasting in Python-based research and enterprise analytics, particularly where transparency and governance are priorities.

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 Prophet's performance and interpretability 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.

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

TL;DR

The paper evaluates Prophet as a reproducibility-enabling forecasting framework for business and financial analytics, emphasizing open workflows, explicit documentation, and controlled experiments rather than new algorithmic development. It compares Prophet against ARIMA variants and Random Forest on Tesla stock prices and store-demand data to illustrate how specification variability affects results and how Prophet can reduce such variability through standardized, script-based processes. Empirical results show Prophet offers strong reproducibility and interpretability, with context-dependent point accuracy relative to traditional benchmarks; it provides consistently calibrated uncertainty intervals, and its component-wise decomposition supports auditability and communication. Overall, Prophet is presented as a robust methodological baseline for reproducible forecasting in Python-based research and enterprise analytics, particularly where transparency and governance are priorities.

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 Prophet's performance and interpretability 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.
Paper Structure (96 sections, 8 equations, 3 figures, 8 tables)

This paper contains 96 sections, 8 equations, 3 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Contributions of This Paper
  3. Paper Organization
  4. Background and Related Work
  5. Reproducibility in Computational Research
  6. Traditional Forecasting Methods
  7. Machine Learning Approaches to Forecasting
  8. Emergence of Open and Reproducible Forecasting Frameworks
  9. Prophet as an Additive and Reproducible Framework
  10. Comparative Positioning of Prophet
  11. Summary of the Research Gap
  12. Data and Experimental Setup
  13. Overview of Experimental Design
  14. Data Sources
  15. Data Description
  16. ...and 81 more sections

Figures (3)

  • Figure 1: Prophet Component Decomposition for Retail Dataset. The figure shows the additive decomposition of forecasts into trend, yearly seasonality, weekly seasonality, and holiday effects.
  • Figure 2: Forecast Comparison Across Models. The figure shows actual values (black dots) and model predictions for Prophet, ARIMA variants, and Random Forest on both financial and retail datasets. Prophet's 95% uncertainty intervals are shown as shaded regions.
  • Figure 3: Metric Comparison Across Models. Bar charts comparing RMSE, MAE, and MAPE for all models on both financial and retail datasets. Lower values indicate better performance.