View source: R/parsnip-exp_smoothing.R
exp_smoothing | R Documentation |
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
exp_smoothing( mode = "regression", seasonal_period = NULL, error = NULL, trend = NULL, season = NULL, damping = NULL, smooth_level = NULL, smooth_trend = NULL, smooth_seasonal = NULL )
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. |
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()
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", persistence = NULL, phi = NULL, initial = c("optimal", ## "backcasting"), initialSeason = NULL, ic = c("AICc", "AIC", "BIC", ## "BICc"), loss = c("likelihood", "MSE", "MAE", "HAM", "MSEh", "TMSE", ## "GTMSE", "MSCE"), h = 10, holdout = FALSE, cumulative = FALSE, interval = c("none", ## "parametric", "likelihood", "semiparametric", "nonparametric"), level = 0.95, ## bounds = c("usual", "admissible", "none"), silent = c("all", "graph", ## "legend", "output", "none"), xreg = NULL, xregDo = 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).
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:
seasonal_period = "auto"
: A period is selected based on the periodicity of the data (e.g. 12 if monthly)
seasonal_period = 12
: A numeric frequency. For example, 12 is common for monthly data
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$y)
will ignore xreg's.
Multivariate (xregs, Exogenous Regressors)
Just for smooth
engine.
The xreg
parameter is populated using the fit()
or fit_xy()
function:
Only factor
, ordered factor
, and numeric
data will be used as xregs.
Date and Date-time variables are not used as xregs
character
data should be converted to factor.
Xreg Example: Suppose you have 3 features:
y
(target)
date
(time stamp),
month.lbl
(labeled month as a ordered factor).
The month.lbl
is an exogenous regressor that can be passed to the arima_reg()
using
fit()
:
fit(y ~ date + month.lbl)
will pass month.lbl
on as an exogenous regressor.
fit_xy(data[,c("date", "month.lbl")], y = data$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
.
fit.model_spec()
, set_engine()
library(dplyr) library(parsnip) library(rsample) library(timetk) library(modeltime) 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 ---- # 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 ---- #' # 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 ---- #' # 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
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