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Should I use Prophet, ARIMA, or gradient boosting for forecasting?
There is no universal winner, so backtest candidates against a baseline on your data. ARIMA and exponential smoothing suit single series with modest data. Prophet-style models handle seasonality and holidays with little tuning. Gradient boosting works well across many related series with rich driver variables.
Rules of thumb:
- Few series, short history, simple patterns: exponential smoothing or ARIMA.
- Strong holiday and seasonal effects with irregular data: a decomposable model like Prophet.
- Hundreds or thousands of related items with promotions, prices and other features: gradient boosting or similar learning models.
Pick on measured accuracy, but also consider speed, ease of explaining, and who will maintain it. A slightly less accurate model people understand and trust is often the better business choice.
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