Choose a Model

Greykite offers two forecasting models: the Prophet model and the Silverkite model. This page explains your options.

high-level comparison

PROPHET

SILVERKITE

speed

slower

faster

forecast accuracy (default)

good

good

forecast accuracy (customized)

limited

high

prediction interval accuracy

TBD

TBD

interpretability

good (additive model)

good (additive model)

ease of use

good

good

API

similar to sklearn

uses sklearn

fit

Bayesian

ridge, elastic net, boosted trees, etc.

Both models have the similar customization options. Differences are bolded below.

customization options

PROPHET

SILVERKITE

automatic defaults

yes

yes

growth

linear, logistic

linear, sqrt, quadratic, any combination, custom

seasonality

daily, weekly, yearly, custom

daily, weekly, monthly, quarterly, yearly

holidays

specify countries, with window

specify by name or country, with window; or custom events

trend changepoints

yes

yes

seasonality changepoints

no

yes

regressors

yes

yes

autoregression

limited, via regressors

full support, coming soon

interaction terms

build it yourself (regressor)

model formula terms, or as regressor

extras

prior scale (bayesian)

fitting algorithm

loss function

MSE

MSE, Quantile loss (with gradient_boosting fitting algorithm)

prediction intervals

yes

yes

Note

When to use the Prophet model?

  • If it works better for your dataset

  • If you like Bayesian models

  • If you need logistic growth with changing capacity over time

Note

When to use the Silverkite model?

  • If it works better for your dataset (e.g. b/c of custom growth, interaction terms, seasonality changepoints).

  • If speed is important.

  • If you want to forecast a quantile, not the mean.

Note

We use Prophet 0.5 (Prophet documentation.)