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Meta Prophet — forecasting at scale. Additive model with yearly/weekly/daily seasonality, holiday effects, changepoints, and trend decomposition. Handles missing data and outliers automatically.
Passed 20 of 23 test cases with the skill, and 20 of 23 without it.
0 fixed20 passed either way3 still failing0 broke
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The instructions your model reads when the skill loads.
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.
uv pip install prophet
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)
model = Prophet()
model.add_country_holidays("US")
model.fit(df)
test cases passed, vs 20 / 23 without
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