seasonal_reg() is a way to generate a specification of an
Seasonal Decomposition model
before fitting and allows the model to be created using
different packages. Currently the only package is forecast.
seasonal_reg(
mode = "regression",
seasonal_period_1 = NULL,
seasonal_period_2 = NULL,
seasonal_period_3 = NULL
)A single character string for the type of model. The only possible value for this model is "regression".
(required) The primary 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.
(optional) A second seasonal frequency.
Is NULL 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.
(optional) A third seasonal frequency.
Is NULL 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.
The standardized parameter names in modeltime can be mapped to their original
names in each engine:
| modeltime | forecast::stlm | forecast::tbats |
| seasonal_period_1, seasonal_period_2, seasonal_period_3 | msts(seasonal.periods) | msts(seasonal.periods) |
Other options can be set using set_engine().
The engines use forecast::stlm().
Function Parameters:
#> function (y, s.window = 7 + 4 * seq(6), robust = FALSE, method = c("ets",
#> "arima"), modelfunction = NULL, model = NULL, etsmodel = "ZZN", lambda = NULL,
#> biasadj = FALSE, xreg = NULL, allow.multiplicative.trend = FALSE, x = y,
#> ...)
tbats
Method: Uses method = "tbats", which by default is auto-TBATS.
Xregs: Univariate. Cannot accept Exogenous Regressors (xregs). Xregs are ignored.
stlm_ets
Method: Uses method = "stlm_ets", which by default is auto-ETS.
Xregs: Univariate. Cannot accept Exogenous Regressors (xregs). Xregs are ignored.
stlm_arima
Method: Uses method = "stlm_arima", which by default is auto-ARIMA.
Xregs: Multivariate. Can accept Exogenous Regressors (xregs).
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
yearly seasonal (e.g. For monthly time stamps, seasonal_period = 12, seasonal_period = "12 months", or seasonal_period = "yearly").
There are 3 ways to specify:
seasonal_period = "auto": A seasonal 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 (No xregs, Exogenous Regressors):
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$y) will ignore xreg's.
Multivariate (xregs, Exogenous Regressors)
The tbats engine cannot accept Xregs.
The stlm_ets engine cannot accept Xregs.
The stlm_arima engine can accept Xregs
The xreg parameter is populated using the fit() or fit_xy() function:
Only factor, ordered factor, and numeric data will be used as xregs.
Date and Date-time variables are not used as xregs
character data should be converted to factor.
Xreg Example: Suppose you have 3 features:
y (target)
date (time stamp),
month.lbl (labeled month as a ordered factor).
The month.lbl is an exogenous regressor that can be passed to the seasonal_reg() using
fit():
fit(y ~ date + month.lbl) will pass month.lbl on as an exogenous regressor.
fit_xy(data[,c("date", "month.lbl")], y = data$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.
The data given to the function are not saved and are only used
to determine the mode of the model. For seasonal_reg(), the
mode will always be "regression".
The model can be created using the fit() function using the
following engines:
"tbats" - Connects to forecast::tbats()
"stlm_ets" - Connects to forecast::stlm(), method = "ets"
"stlm_arima" - Connects to forecast::stlm(), method = "arima"
fit.model_spec(), set_engine()
library(dplyr)
library(parsnip)
library(rsample)
library(timetk)
library(modeltime)
# Data
taylor_30_min
# Split Data 80/20
splits <- initial_time_split(taylor_30_min, prop = 0.8)
# ---- STLM ETS ----
# Model Spec
model_spec <- seasonal_reg() %>%
set_engine("stlm_ets")
# Fit Spec
model_fit <- model_spec %>%
fit(log(value) ~ date, data = training(splits))
model_fit
# ---- STLM ARIMA ----
# Model Spec
model_spec <- seasonal_reg() %>%
set_engine("stlm_arima")
# Fit Spec
model_fit <- model_spec %>%
fit(log(value) ~ date, data = training(splits))
model_fit
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