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📈 SARIMA and Prophet: Two Ways to Model Seasonality

A 3D forecasting lab comparing how SARIMA and Prophet extend a seasonal time series into the future, including how each handles holiday spikes and growing uncertainty.

AI & Machine Learning3DAdvanced60 FPS

A synthetic monthly time series with trend, seasonality and an optional December spike is extended into the future by two forecast ribbons — SARIMA in orange, Prophet in blue — so you can see exactly where their mechanics diverge.

🔬 What It Demonstrates

Both models extrapolate the same learned trend and seasonal cycle, but SARIMA has no holiday term and smooths straight through the December spike, while Prophet's explicit holiday regressor reproduces it. Their uncertainty bands also widen at different rates.

🎮 How to Use

Adjust seasonal amplitude, trend slope and noise to reshape the observed history, set the forecast horizon, and toggle the holiday effect to watch the two forecast ribbons agree or disagree. Resample noise for a fresh random history.

💡 Did You Know?

Facebook's Core Data Science team built Prophet in 2017 because analysts kept hand-engineering holiday dummies into ARIMA models — Prophet turns that workaround into a first-class, tunable model component.

⚙ Under the hood

A 3D forecasting lab comparing how SARIMA and Prophet extend a seasonal time series into the future, including how each handles holiday spikes and growing uncertainty.

time series analysisforecastingseasonalityprophetsarimamachine learningpredictionThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install