Choose a Model

Greykite offers a few forecasting models: Silverkite, Prophet, and ARIMA. This page provides an overview.

high-level comparison

	SILVERKITE	PROPHET	ARIMA
speed	fast	slow	fast
forecast accuracy (default)	decent	decent	decent
forecast accuracy (customized)	very good	good	good
interpretability	good	good	decent
ease of use	good	good	very good
API	sklearn	similar to sklearn	similar to sklearn

Like Prophet, Silverkite includes intepretable terms for growth, seasonality, holidays, trend changepoints, and regressors. Silverkite also supports autoregression, seasonality changepoints, easy-to-use interaction terms, quantile loss, and custom fit algorithms. This makes Silverkite flexible to capture different time series patterns.

customization options

	SILVERKITE	PROPHET
automatic defaults	yes	yes
growth	linear, sqrt, quadratic, any combination, custom	linear, logistic
seasonality	daily, weekly, monthly, quarterly, yearly	daily, weekly, yearly, custom
holidays	specify names or countries, with window; or custom events	specify countries, with window
regressors	yes	yes
trend changepoints	yes	yes
seasonality changepoints	yes	no
autoregression	yes	limited (via regressors)
interaction terms	yes (via model formula or regressors)	limited (via regressors)
loss function	MSE, Quantile loss	MSE
fit algorithm	custom (ridge, quantile regression, etc.)	fixed (Bayesian formulation)

Note

When to use the Silverkite model?

• If both speed and interpretability are important

• If you need flexible tuning options to achieve high accuracy

• If you need to forecast a quantile

Note

When to use the Prophet model?

• If you need logistic growth with changing capacity over time

• If speed is not as important

Note

When to use the ARIMA model?

• If you want to try a classic algorithm that is different from the other two

• If you want to quickly establish an accuracy baseline to assess forecast difficulty

• If interpretability is not as important