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