Facebook Prophet

Prophet is a time series forecasting tool open sourced by Facebook that aims to account for various seasonality of behavior as well as auto detecting change points and anticipating different behavior during holidays. Details can be found on the Prophet page.

Warning

This is still a work in progress. Currently the plugin shells out to a Python script that runs the Prophet python module. It’s less than efficient but it’s enough to get started building a UI.

Fields specific to the prophet config include:

Name	Data Type	Required	Description	Default	Example
numberofChangePoints	Numeric	Optional	The number of anticpated change points spread evenly across the historical data. Must be smaller than the number of points fed into the training algo.	25	1440
growth	String	Optional	One of LINEAR, LOGISTIC, FLAT. The anticipated change in values over time, e.g. growing linearly or logarithmically.	LINEAR	LOGISTIC
changePointRange	Numeric	Optional	Proportion of history in which trend changepoints will be estimated. Defaults to 0.8 for the first 80%. Not used if changepoints (not implemented yet) is specified.	0.80	0.50
yearlySeasonality	Boolean	Optional	Whether or not to look for yearly seasonality. If null, defaults to auto detection.	null	true
weeklySeasonality	Boolean	Optional	Whether or not to look for weekly seasonality. If null, defaults to auto detection.	null	true
dailySeasonality	Boolean	Optional	Whether or not to look for daily seasonality. If null, defaults to auto detection.	null	true
seasonalityMode	String	Optional	One of ADDITIVE or MULTIPLICATIVE. TODO not sure what it is.	ADDITIVE	MULTIPLICATIVE
seasonalityPriorScale	Numeric	Optional	Parameter modulating the strength of the seasonality model. Larger values allow the model to fit larger seasonal fluctuations, smaller values dampen the seasonality. Can be specified for individual seasonalities using add_seasonality.	10	25
holidayPriorScale	Numeric	Optional	Parameter modulating the strength of the holiday components model, unless overridden in the holidays input.	10	25
changepointPriorScale	Numeric	Optional	Parameter modulating the flexibility of the automatic changepoint selection. Large values will allow many changepoints, small values will allow few changepoints.	0.05	0.10
mcmcSamples	Numeric	Optional	If greater than 0, will do full Bayesian inference with the specified number of MCMC samples. If 0, will do MAP estimation	0	5
uncertaintyIntervalWidth	Numeric	Optional	Width of the uncertainty intervals provided for the forecast. If mcmcSamples = 0, this will be only the uncertainty in the trend using the MAP estimate of the extrapolated generative model. If mcmcSamples > 0, this will be integrated over all model parameters, which will include uncertainty in seasonality.	0.8	0.5
uncertaintySamples	Numeric	Optional	Number of simulated draws used to estimate uncertainty intervals. Settings this value to 0 or False will disable uncertainty estimation and speed up the calculation.	1000	0