Prophet Parameters

Key hyperparameters for tuning Facebook Prophet.

Core Parameters

Parameter	Default	Effect
growth	'linear'	'linear' for linear trend, 'logistic' for saturating growth (requires cap column)
changepoint_prior_scale	0.05	Controls trend flexibility. Higher values → more changepoints → risk of overfitting the trend
seasonality_prior_scale	10	Controls seasonality flexibility. Higher values → more aggressive seasonal fitting
holidays_prior_scale	10	Controls holiday effect flexibility
seasonality_mode	'additive'	'additive' or 'multiplicative' — use multiplicative when seasonal amplitude grows with the trend

Changepoints

Prophet automatically detects trend changepoints (points where the growth rate shifts). You can control this with:

• n_changepoints: Number of potential changepoints (default 25).
• changepoint_range: Proportion of the series in which changepoints are placed (default 0.8 — last 20% is excluded to avoid overfitting the tail).

Custom Seasonality

from prophet import Prophet

model = Prophet()

# Add custom seasonality (e.g., monthly with 30.5 day period)
model.add_seasonality(name="monthly", period=30.5, fourier_order=5)

Tuning Example

from prophet import Prophet
import pandas as pd
import numpy as np

# Create sample data
dates = pd.date_range("2020-01-01", periods=365 * 3, freq="D")
y = np.sin(np.linspace(0, 6 * np.pi, len(dates))) * 10 + np.linspace(50, 100, len(dates))
df = pd.DataFrame({"ds": dates, "y": y + np.random.default_rng(42).normal(0, 2, len(dates))})

model = Prophet(
    changepoint_prior_scale=0.1,   # More flexible trend
    seasonality_prior_scale=5,     # Moderate seasonality
    seasonality_mode="additive"
)
model.fit(df)

future = model.make_future_dataframe(periods=90)
forecast = model.predict(future)
model.plot(forecast)

Workplace Tip

Start with default parameters. If the trend looks too rigid, increase changepoint_prior_scale. If seasonality looks noisy, decrease seasonality_prior_scale.

KSB Mapping

KSB	Description	How This Addresses It
K4.1	Statistical models and methods	ARIMA, SARIMA, and exponential smoothing foundations
K4.2	Predictive analytics and ML techniques	Time series forecasting and model comparison
K5.3	Common patterns in real-world data	Identifying trends, seasonality, and stationarity
S1	Scientific methods and hypothesis testing	Stationarity testing, model diagnostics, forecast validation
S4	Analysis and models to inform outcomes	Building forecasts to support business planning
B5	Impartial, hypothesis-driven approach	Honest evaluation of forecast accuracy and limitations