modeltime (version 1.3.0)

exp_smoothing: General Interface for Exponential Smoothing State Space Models

Description

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. Several algorithms are implemented:

• ETS - Automated Exponential Smoothing

• CROSTON - Croston's forecast is a special case of Exponential Smoothing for intermittent demand

• Theta - A special case of Exponential Smoothing with Drift that performed well in the M3 Competition

Usage

exp_smoothing(
  mode = "regression",
  seasonal_period = NULL,
  error = NULL,
  trend = NULL,
  season = NULL,
  damping = NULL,
  smooth_level = NULL,
  smooth_trend = NULL,
  smooth_seasonal = 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".

smooth_level

This is often called the "alpha" parameter used as the base level smoothing factor for exponential smoothing models.

smooth_trend

This is often called the "beta" parameter used as the trend smoothing factor for exponential smoothing models.

smooth_seasonal

This is often called the "gamma" parameter used as the seasonal smoothing factor for exponential smoothing models.

Engine Details

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

modeltime	forecast::ets	forecast::croston()	forecast::thetaf()	smooth::es()
seasonal_period()	ts(frequency)	ts(frequency)	ts(frequency)	ts(frequency)
error(), trend(), season()	model ('ZZZ')	NA	NA	model('ZZZ')
damping()	damped (NULL)	NA	NA	phi
smooth_level()	alpha (NULL)	alpha (0.1)	NA	persistence(alpha)
smooth_trend()	beta (NULL)	NA	NA	persistence(beta)
smooth_seasonal()	gamma (NULL)	NA	NA	persistence(gamma)

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

• smooth_level(), smooth_trend(), and smooth_seasonal() are automatically determined if not provided. They are mapped to "alpha", "beta" and "gamma", respectively.

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.

croston

The engine uses forecast::croston().

Function Parameters:

#> function (y, h = 10, alpha = 0.1, x = y)

The main arguments are defined using:

• smooth_level(): The "alpha" parameter

Parameter Notes:

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

theta

The engine uses forecast::thetaf()

Parameter Notes:

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

smooth_es

The engine uses smooth::es().

Function Parameters:

#> function (y, model = "ZZZ", lags = c(frequency(y)), persistence = NULL, 
#>     phi = NULL, initial = c("optimal", "backcasting", "complete"), initialSeason = NULL, 
#>     ic = c("AICc", "AIC", "BIC", "BICc"), loss = c("likelihood", "MSE", 
#>         "MAE", "HAM", "MSEh", "TMSE", "GTMSE", "MSCE"), h = 10, holdout = FALSE, 
#>     bounds = c("usual", "admissible", "none"), silent = TRUE, xreg = NULL, 
#>     regressors = c("use", "select"), initialX = NULL, ...)

The main arguments model and phi are defined using:

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

• trend() = "auto", "additive", "multiplicative", "additive_damped", "multiplicative_damped" and "none" are converted to "Z", "A", "M", "Ad", "Md" and "N".

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

• damping() - Value of damping parameter. If NULL, then it is estimated.

• smooth_level(), smooth_trend(), and smooth_seasonal() are automatically determined if not provided. They are mapped to "persistence"("alpha", "beta" and "gamma", respectively).

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

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

Parameter Notes:

• xreg - This is supplied via the parsnip / modeltime fit() interface (so don't provide this manually). See Fit Details (below).

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.

• fit(y ~ date)

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:

• Formula Interface (recommended): fit(y ~ date) will ignore xreg's.

• XY Interface: fit_xy(x = data[,"date"], y = data$y) will ignore xreg's.

Multivariate (xregs, Exogenous Regressors)

Just for smooth engine.

The xreg parameter is populated using the fit() or fit_xy() function:

• Only factor, ordered factor, and numeric data will be used as xregs.

• Date and Date-time variables are not used as xregs

• character data should be converted to factor.

Xreg Example: Suppose you have 3 features:

1. y (target)

2. date (time stamp),

3. month.lbl (labeled month as a ordered factor).

The month.lbl is an exogenous regressor that can be passed to the arima_reg() using fit():

• fit(y ~ date + month.lbl) will pass month.lbl on as an exogenous regressor.

• fit_xy(data[,c("date", "month.lbl")], y = data$y) will pass x, where x is a data frame containing month.lbl and the date feature. Only month.lbl will be used as an exogenous regressor.

Note that date or date-time class values are excluded from xreg.

Details

Models can be created using the following engines:

• "ets" (default) - Connects to forecast::ets()

• "croston" - Connects to forecast::croston()

• "theta" - Connects to forecast::thetaf()

• "smooth_es" - Connects to smooth::es()

See Also

fit.model_spec(), set_engine()

Examples

if (FALSE) { # rlang::is_installed("smooth")
library(dplyr)
library(parsnip)
library(rsample)
library(timetk)
library(smooth)

# Data
m750 <- m4_monthly %>% filter(id == "M750")
m750

# 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))
model_fit


# ---- 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


# ---- CROSTON ----
# \donttest{
# Model Spec
model_spec <- exp_smoothing(
        smooth_level = 0.2
    ) %>%
    set_engine("croston")

# Fit Spec
model_fit <- model_spec %>%
    fit(log(value) ~ date, data = training(splits))
model_fit
# }



# ---- THETA ----
# \donttest{
#' # Model Spec
model_spec <- exp_smoothing() %>%
    set_engine("theta")

# Fit Spec
model_fit <- model_spec %>%
    fit(log(value) ~ date, data = training(splits))
model_fit
# }




#' # ---- SMOOTH ----
# \donttest{
#' # Model Spec
model_spec <- exp_smoothing(
               seasonal_period  = 12,
               error            = "multiplicative",
               trend            = "additive_damped",
               season           = "additive"
         ) %>%
    set_engine("smooth_es")

# Fit Spec
model_fit <- model_spec %>%
    fit(value ~ date, data = training(splits))
model_fit
# }
}