Prophet

Prophet represents observations through a trend, recurring seasonal patterns and optional holiday or regressor effects. Trend can include changepoints, and seasonal components use a flexible periodic representation. The library provides fitting, future timestamp construction and prediction interfaces, with additive or multiplicative components for suitable settings. The component view makes it possible to inspect assumptions about recurring behavior and changes in growth. Prophet is a specific forecasting model and implementation, rather than a general name for automated forecasting. External regressors still require values at forecast time, and the model's decomposition should be checked against the process being forecast.

Prepare consistent timestamps and numeric outcomes, investigate gaps and decide how special events should be represented. Choose trend behavior, seasonality and changepoint flexibility rather than relying blindly on defaults. Fit only historical data available at each backtest date and compare errors against naive and seasonal baselines. Inspect component plots and prediction intervals, especially near structural changes. A useful deliverable includes a repeatable forecast workflow and a record of component choices, making clear which future assumptions were supplied by an analyst and which were learned from observations.

Flexible components can fit historical noise, and sudden regime changes may not follow the extrapolated trend. Intervals reflect model assumptions and need empirical coverage checks. Holidays and regressors can leak future information if supplied retrospectively in a backtest. A component plot is not causal attribution. Prophet should be compared with alternative forecasting approaches under the same horizon and information set; ease of fitting alone does not establish that its decomposition is suitable for an irregular or very short series.