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What Prophet does
Prophet is an open-source forecasting procedure and Python package for time series that can be modeled with a trend, seasonal patterns, holidays, and optional external regressors. Its Python interface follows a scikit-learn-style fit-and-predict pattern. Install the package as prophet:
python -m pip install prophetThe model represents a forecast through interpretable components. Trend describes longer-term movement; seasonality captures recurring patterns; holidays represent known calendar effects; and regressors let you include other explanatory variables. Which components help depends on the series and on what information will actually be available when you make a forecast.
Prepare the data Prophet expects
Provide a pandas-compatible DataFrame with two conceptually required columns:
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ds: dates or timestamps representing when each observation occurred.y: numeric values for the quantity to forecast.
Use one row per observed time point. Make sure the date values are parsed consistently and the target values are numeric. Keep other columns only when you intend to use them as supported holidays or regressors; Prophet’s basic target input is the ds/y pair.
For example, a daily sales dataset could look like this:
import pandas as pd
# Example structure; replace these values with your observations.
df = pd.DataFrame({
"ds": pd.to_datetime(["2026-01-01", "2026-01-02", "2026-01-03"]),
"y": [120, 135, 128],
})Fit a model and generate forecasts
The core workflow is short: initialize a model, fit it to historical observations, create the dates to forecast, and predict on the combined historical and future dates.
- Import Prophet and fit the model. Call
fitwith the DataFrame containingdsandy. - Create future dates. Call
make_future_dataframe(periods=30)to append 30 periods beyond the history, using the inferred data frequency unless you specify one. - Predict. Pass the date DataFrame to
predict.
from prophet import Prophet
m = Prophet()
m.fit(df) # df contains ds and y
future = m.make_future_dataframe(periods=30)
forecast = m.predict(future)
print(forecast[["ds", "yhat", "yhat_lower", "yhat_upper"]].tail())The forecast DataFrame includes yhat, the central forecast, uncertainty bounds, and component columns. Choose the number of future periods and the date frequency to match the practical question: forecasting 30 daily observations is different from forecasting 30 months.
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Choose trend, seasonality, holidays, and regressors
Prophet exposes configuration for several forecast components. Treat these as modeling decisions rather than universal defaults: select settings based on the series, domain knowledge, and validation performance.
Growth and changepoints
Growth can be configured as linear, logistic, or flat. Linear growth is suitable when a continuing trend is plausible without a known fixed ceiling. Logistic growth is useful when the quantity has a meaningful saturation limit; it requires specifying the capacity information used by that model. Flat growth is an option when the series has no underlying trend. Ch changepoint controls affect how readily the fitted trend can adapt to changes in direction or rate: greater flexibility may capture genuine shifts, but can also fit noise.
Recurring seasonal patterns
Prophet supports yearly, weekly, daily, and custom seasonalities. Enable or add a seasonal component when the data has a recurring pattern at that period and enough history to estimate it. For example, weekly seasonality may be relevant for daily observations with weekday effects; a custom period can represent a recurring cycle not covered by the built-in choices. Additive seasonality models an effect in the target’s units, while multiplicative seasonality models an effect proportional to the trend.
Known holidays and calendar events
Supply a holidays DataFrame for known calendar effects you expect to influence the target. This is appropriate for events with dates that are known during both training and forecasting. Holiday effects are distinct from general recurring seasonality: a holiday can shift a forecast on particular dates even when the surrounding weekly or yearly pattern is otherwise stable.
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Use an extra regressor when another variable may help explain the target, such as a promotion indicator or a known operating condition. Its values must be available for every date being predicted. That requirement also applies during validation: a regressor that is only known after the forecast date cannot be supplied honestly for a historical forecast horizon unless you have a separate way to forecast it.
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Regularization and prior scales
Prior-scale settings regularize component estimates, including seasonality and holidays. Stronger regularization can restrain components that would otherwise respond too freely to limited or noisy data; weaker regularization gives them more room to fit. Compare candidate settings using historical forecasts at the horizon you intend to use instead of selecting them from training fit alone.
Understand Prophet’s uncertainty intervals
Predictions include yhat_lower and yhat_upper around yhat. Prophet’s documented uncertainty sources include future trend changes, uncertainty in estimated seasonality, and observation noise. The default interval_width is 0.8, corresponding to an 80% interval. Changing that width changes the interval bounds, not the central yhat forecast. These bounds express model-based uncertainty; they are not guarantees that a future observation will fall inside them.
Measure forecast accuracy with rolling cross-validation
Evaluate predictions on data that was not used to fit each forecast. Prophet’s historical cross-validation selects cutoff dates, fits the model only on observations before each cutoff, and forecasts the chosen horizon. The initial argument sets the first training span, while period controls the spacing between cutoffs.
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from prophet.diagnostics import cross_validation, performance_metrics
# Choose initial, period, and horizon to suit your history and use case.
df_cv = cross_validation(
m,
initial="730 days",
period="180 days",
horizon="365 days",
)
df_metrics = performance_metrics(df_cv)
print(df_metrics[["horizon", "rmse", "mae", "mape", "coverage"]].head())The example values above are illustrative settings, not recommendations for every series. A useful initial span should contain enough history to fit the patterns you need; the horizon should match the lead time of the forecast you will actually make. Ensure the dataset covers enough history to create meaningful cutoff-and-horizon pairs.
performance_metrics summarizes errors and interval behavior, including RMSE, MAE, MAPE, and coverage. Compare settings at equivalent forecast horizons: performance can deteriorate as the forecast extends further out, so a single averaged score can obscure where a model is useful. Review errors around trend changes and important seasonal or holiday periods as well as aggregate metrics.
Prophet’s documentation example reports errors around 5% at a one-month horizon, increasing to about 11% at a one-year horizon for that example series. Those are results for the documented example data, not a general accuracy guarantee. Your series, horizon, data quality, and configuration determine the performance you should expect.
How to compare Prophet configurations
When comparing settings or another forecasting approach, keep the evaluation period and forecast horizon consistent. Consider:
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- Interval coverage: whether observed values fall within the predicted ranges at an appropriate rate.
- Behavior around trend changes and unusual periods.
- Whether multiple seasonalities and known holidays are represented usefully.
- How missing or irregular observations affect the available history and forecast dates.
- Computational cost and the future availability of any regressor values.
The most useful model is not necessarily the one with the closest fit to historical observations; it is the one that performs acceptably on realistic held-out forecasts and can be supplied with the information available at prediction time.
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