Modern Forecasting Models: Prophet, Regression/ARIMAX, and Machine Learning

2026-06-22 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, extended

Summary

This article explores three modern forecasting approaches: Prophet, Regression/ARIMAX, and Machine Learning models (XGBoost & LSTM), designed to overcome the limitations of classic linear models. Prophet, an open-source tool from Meta, excels at handling holidays, multiple seasonalities, and missing data with minimal tuning, breaking down time series into trend, seasonality, and holiday components. Regression/ARIMAX integrates external variables like price or promotions to explain causality and improve accuracy, though it requires future values for these exogenous factors. Finally, XGBoost and LSTM are introduced for large datasets with complex nonlinear patterns, with XGBoost transforming time series into a feature-rich table and LSTM utilizing neural networks with memory for sequential data. The article emphasizes choosing the right model based on data characteristics and problem complexity, cautioning against using complex ML models for small datasets.

Key takeaway

For Data Scientists and Analysts evaluating forecasting solutions, prioritize model selection based on data volume, pattern complexity, and the need for causal insights. If your data has strong seasonality and holidays, Prophet offers quick, interpretable results. When external factors drive your target, ARIMAX provides valuable causal explanations. Reserve XGBoost or LSTM for large datasets with highly nonlinear patterns, but always benchmark against simpler models to avoid overfitting and unnecessary complexity on smaller data.

Key insights

Selecting the optimal forecasting model hinges on data characteristics, complexity, and the need for interpretability or causal explanation.

Principles

• Prophet automatically handles multiple seasonalities and holidays.
• ARIMAX explains causality through external variable coefficients.
• ML models excel on large, complex, nonlinear datasets.

Method

XGBoost transforms time series into a feature table using lags, moving statistics, and calendar features for regression. LSTM processes sequential data with internal memory for long-term dependencies.

In practice

• Use Prophet for e-commerce sales with many events.
• Apply ARIMAX for marketing impact analysis with controlled variables.
• Employ XGBoost for large-scale demand forecasting across many SKUs.

Topics

• Time Series Forecasting
• Prophet Model
• ARIMAX
• XGBoost
• LSTM
• Machine Learning

Best for: Data Scientist, Data Analyst, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.