Prophet

Type

ml-estimator

Class

fire.nodes.ts.NodeProphet

Fields

Name	Title	Description
ds	DS Column	Date or DateTime variable
y	Y	Target Variable
growth	Growth	linear or logistic to specify a linear or logistic trend
yearly_seasonality	Yearly Seasonality	Fit yearly seasonality.
weekly_seasonality	Weekly Seasonality	Fit weekly seasonality.
daily_seasonality	Daily Seasonality	Fit daily seasonality.
seasonality_mode	Seasonality Mode	additive(default) or multiplicative
interval_width	Interval Width	Float, width of the uncertainty intervals provided for the forecast
changepoints	Changepoints	List of dates at which to include potential changepoints. If not specified, potential changepoints are selected automatically.
changepoints_format	Changepoints Format	datetime format for the comma seperated Changepoint specified.
n_changepoints	N Changepoints	Number of potential changepoints to include. Not used if input changepoints is supplied. If changepoints is not supplied, then n_changepoints potential changepoints are selected uniformly from the first changepoint_range proportion of the history.
changepoint_range	Changepoint Range	Proportion of history in which trend changepoints will be estimated. Defaults to 0.8 for the first 80%. Not used if changepoints is specified.
seasonality_prior_scale	Seasonality Prior Scale	Parameter modulating the strength of the seasonality model. Larger values allow the model to fit larger seasonal fluctuations, smaller values dampen the seasonality.
holidays_prior_scale	Holidays Prior Scale	Parameter modulating the strength of the holiday components model, unless overridden in the holidays input. changepoint_prior_scale: Parameter modulating the flexibility of the automatic changepoint selection. Large values will allow many changepoints, small values will allow few changepoints.
changepoint_prior_scale	Changepoint Prior Scale	Parameter modulating the flexibility of the automatic changepoint selection. Large values will allow many changepoints, small values will allow few changepoints.
mcmc_samples	mcmc samples	Integer, if greater than 0, will do full Bayesian inference with the specified number of MCMC samples. If 0, will do MAP estimation.
uncertainty_samples	Uncertainty Samples	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.
custom_seasonality	Custom Seasonality	Add a seasonal component with specified period, number of Fourier components, and prior scale. Input will be of form name=monthly,period=30.5,fourier_order=5,prior_scale=0.9(optional),mode=additive(optional).
regressor	Regressor	Add an additional regressor to be used for fitting and predicting. Input will be of form name=column_name1,prior_scale=30.5(optional),standardize=auto(optional),mode=additive(optional):name=column_name2,prior_scale=40.5(optional),standardize=auto(optional),mode=additive(optional)
country_holidays	Country Holidays	Built-in country holidays can only be set for a single country. Takes country as input like US

Examples

Prophet Node Examples

Example 1: Predicting Product Demand

Input Schema:

DS Column: “Date” (e.g., daily timestamps)

Y: “Demand” (e.g., the number of products sold daily)

Other relevant columns are ignored in this configuration.

Configuration:

Growth: linear

Yearly Seasonality: auto

Weekly Seasonality: auto

Seasonality Mode: additive

Output:

Forecasted demand values with confidence intervals for future dates.

Example 2: Forecasting Revenue Growth

Input Schema:

DS Column: “Month” (e.g., monthly timestamps)

Y: “Revenue” (e.g., total revenue per month)

Configuration:

Growth: logistic

Specify upper limit for growth as a model parameter.

Enable Yearly Seasonality for capturing annual patterns.

Output:

Predicted revenue growth with changepoints indicating shifts in trends.