exp_smoothing() is a way to generate a specification of an Exponential Smoothing model before fitting and allows the model to be created using different packages. Currently the only package is forecast.

exp_smoothing(
  mode = "regression",
  seasonal_period = NULL,
  error = NULL,
  trend = NULL,
  season = NULL,
  damping = NULL
)

Arguments

mode

A single character string for the type of model. The only possible value for this model is "regression".

seasonal_period

A seasonal frequency. Uses "auto" by default. A character phrase of "auto" or time-based phrase of "2 weeks" can be used if a date or date-time variable is provided. See Fit Details below.

error

The form of the error term: "auto", "additive", or "multiplicative". If the error is multiplicative, the data must be non-negative.

trend

The form of the trend term: "auto", "additive", "multiplicative" or "none".

season

The form of the seasonal term: "auto", "additive", "multiplicative" or "none"..

damping

Apply damping to a trend: "auto", "damped", or "none".

Details

The data given to the function are not saved and are only used to determine the mode of the model. For exp_smoothing(), the mode will always be "regression".

The model can be created using the fit() function using the following engines:

Engine Details

The standardized parameter names in modeltime can be mapped to their original names in each engine:

modeltimeforecast::ets
seasonal_period()ts(frequency)
error(), trend(), season()model
damping()damped

Other options can be set using set_engine().

ets (default engine)

The engine uses forecast::ets().

Function Parameters:

## function (y, model = "ZZZ", damped = NULL, alpha = NULL, beta = NULL, gamma = NULL, 
##     phi = NULL, additive.only = FALSE, lambda = NULL, biasadj = FALSE, 
##     lower = c(rep(1e-04, 3), 0.8), upper = c(rep(0.9999, 3), 0.98), opt.crit = c("lik", 
##         "amse", "mse", "sigma", "mae"), nmse = 3, bounds = c("both", "usual", 
##         "admissible"), ic = c("aicc", "aic", "bic"), restrict = TRUE, allow.multiplicative.trend = FALSE, 
##     use.initial.values = FALSE, na.action = c("na.contiguous", "na.interp", 
##         "na.fail"), ...)

The main arguments are model and damped are defined using:

  • error() = "auto", "additive", and "multiplicative" are converted to "Z", "A", and "M"

  • trend() = "auto", "additive", "multiplicative", and "none" are converted to "Z","A","M" and "N"

  • season() = "auto", "additive", "multiplicative", and "none" are converted to "Z","A","M" and "N"

  • damping() - "auto", "damped", "none" are converted to NULL, TRUE, FALSE

By default, all arguments are set to "auto" to perform automated Exponential Smoothing using in-sample data following the underlying forecast::ets() automation routine.

Other options and argument can be set using set_engine().

Parameter Notes:

  • xreg - This model is not set up to use exogenous regressors. Only univariate models will be fit.

Fit Details

Date and Date-Time Variable

It's a requirement to have a date or date-time variable as a predictor. The fit() interface accepts date and date-time features and handles them internally.

Seasonal Period Specification

The period can be non-seasonal (seasonal_period = 1 or "none") or seasonal (e.g. seasonal_period = 12 or seasonal_period = "12 months"). There are 3 ways to specify:

  1. seasonal_period = "auto": A period is selected based on the periodicity of the data (e.g. 12 if monthly)

  2. seasonal_period = 12: A numeric frequency. For example, 12 is common for monthly data

  3. seasonal_period = "1 year": A time-based phrase. For example, "1 year" would convert to 12 for monthly data.

Univariate:

For univariate analysis, you must include a date or date-time feature. Simply use:

Multivariate (xregs, Exogenous Regressors)

This model is not set up for use with exogenous regressors.

See also

Examples

library(dplyr) library(parsnip) library(rsample) library(timetk) library(modeltime) # Data m750 <- m4_monthly %>% filter(id == "M750") m750
#> # A tibble: 306 x 3 #> id date value #> <fct> <date> <dbl> #> 1 M750 1990-01-01 6370 #> 2 M750 1990-02-01 6430 #> 3 M750 1990-03-01 6520 #> 4 M750 1990-04-01 6580 #> 5 M750 1990-05-01 6620 #> 6 M750 1990-06-01 6690 #> 7 M750 1990-07-01 6000 #> 8 M750 1990-08-01 5450 #> 9 M750 1990-09-01 6480 #> 10 M750 1990-10-01 6820 #> # ... with 296 more rows
# Split Data 80/20 splits <- initial_time_split(m750, prop = 0.8) # ---- AUTO ETS ---- # Model Spec - The default parameters are all set # to "auto" if none are provided model_spec <- exp_smoothing() %>% set_engine("ets") # Fit Spec model_fit <- model_spec %>% fit(log(value) ~ date, data = training(splits))
#> frequency = 12 observations per 1 year
model_fit
#> parsnip model object #> #> Fit time: 1.1s #> ETS(A,A,A) #> #> Call: #> forecast::ets(y = outcome, model = model_ets, damped = damping_ets) #> #> Smoothing parameters: #> alpha = 0.5893 #> beta = 1e-04 #> gamma = 0.1771 #> #> Initial states: #> l = 8.7377 #> b = 0.002 #> s = 0.029 0.0259 0.0144 -0.0272 -0.1369 -0.0764 #> 0.0209 0.0358 0.036 0.035 0.0274 0.016 #> #> sigma: 0.0186 #> #> AIC AICc BIC #> -584.7384 -582.0304 -525.2865
# ---- STANDARD ETS ---- # Model Spec model_spec <- exp_smoothing( seasonal_period = 12, error = "multiplicative", trend = "additive", season = "multiplicative" ) %>% set_engine("ets") # Fit Spec model_fit <- model_spec %>% fit(log(value) ~ date, data = training(splits)) model_fit
#> parsnip model object #> #> Fit time: 250ms #> ETS(M,Ad,M) #> #> Call: #> forecast::ets(y = outcome, model = model_ets, damped = damping_ets) #> #> Smoothing parameters: #> alpha = 0.5889 #> beta = 0.0065 #> gamma = 0.203 #> phi = 0.98 #> #> Initial states: #> l = 8.7353 #> b = 0.0054 #> s = 1.0027 1.0025 1.0012 0.9972 0.9839 0.9921 #> 1.0024 1.0041 1.0045 1.0039 1.0033 1.0022 #> #> sigma: 0.0021 #> #> AIC AICc BIC #> -576.9488 -573.9088 -513.9998