| name | prophet |
| description | Meta Prophet — forecasting at scale. Additive model with yearly/weekly/daily seasonality, holiday effects, changepoints, and trend decomposition. Handles missing data and outliers automatically. |
| tags | ["prophet","forecasting","time-series","seasonality","trend","meta","zorai"] |
Overview
Meta Prophet forecasts time series data with additive seasonality (yearly, weekly, daily), holiday effects, changepoint detection, and trend decomposition. Handles missing data and outliers automatically. Designed for business forecasting with human-interpretable components.
Installation
uv pip install prophet
Forecast
import pandas as pd
from prophet import Prophet
import numpy as np
df = pd.DataFrame({
"ds": pd.date_range("2023-01-01", periods=365, freq="D"),
"y": [100 + i*0.5 + 10*(i%7==0) + np.random.normal(0, 5) for i in range(365)],
})
model = Prophet(yearly_seasonality=True, weekly_seasonality=True)
model.fit(df)
future = model.make_future_dataframe(periods=90)
forecast = model.predict(future)
model.plot(forecast)
model.plot_components(forecast)
Holidays
model = Prophet()
model.add_country_holidays("US")
model.fit(df)
References
