Description Usage Arguments Details Engine Details Fit Details See Also Examples
View source: R/parsniparima_reg.R
arima_reg()
is a way to generate a specification of an ARIMA model
before fitting and allows the model to be created using
different packages. Currently the only package is forecast
.
1 2 3 4 5 6 7 8 9 10  arima_reg(
mode = "regression",
seasonal_period = NULL,
non_seasonal_ar = NULL,
non_seasonal_differences = NULL,
non_seasonal_ma = NULL,
seasonal_ar = NULL,
seasonal_differences = NULL,
seasonal_ma = 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 timebased phrase of "2 weeks" can be used if a date or datetime variable is provided. See Fit Details below. 
non_seasonal_ar 
The order of the nonseasonal autoregressive (AR) terms. Often denoted "p" in pdqnotation. 
non_seasonal_differences 
The order of integration for nonseasonal differencing. Often denoted "d" in pdqnotation. 
non_seasonal_ma 
The order of the nonseasonal moving average (MA) terms. Often denoted "q" in pdqnotation. 
seasonal_ar 
The order of the seasonal autoregressive (SAR) terms. Often denoted "P" in PDQnotation. 
seasonal_differences 
The order of integration for seasonal differencing. Often denoted "D" in PDQnotation. 
seasonal_ma 
The order of the seasonal moving average (SMA) terms. Often denoted "Q" in PDQnotation. 
The data given to the function are not saved and are only used
to determine the mode of the model. For arima_reg()
, the
mode will always be "regression".
The model can be created using the fit()
function using the
following engines:
"auto_arima" (default)  Connects to forecast::auto.arima()
"arima"  Connects to forecast::Arima()
Main Arguments
The main arguments (tuning parameters) for the model are:
seasonal_period
: The periodic nature of the seasonality. Uses "auto" by default.
non_seasonal_ar
: The order of the nonseasonal autoregressive (AR) terms.
non_seasonal_differences
: The order of integration for nonseasonal differencing.
non_seasonal_ma
: The order of the nonseasonal moving average (MA) terms.
seasonal_ar
: The order of the seasonal autoregressive (SAR) terms.
seasonal_differences
: The order of integration for seasonal differencing.
seasonal_ma
: The order of the seasonal moving average (SMA) terms.
These arguments are converted to their specific names at the time that the model is fit.
Other options and argument can be
set using set_engine()
(See Engine Details below).
If parameters need to be modified, update()
can be used
in lieu of recreating the object from scratch.
The standardized parameter names in modeltime
can be mapped to their original
names in each engine:
modeltime  forecast::auto.arima  forecast::Arima 
seasonal_period  ts(frequency)  ts(frequency) 
non_seasonal_ar, non_seasonal_differences, non_seasonal_ma  max.p(5), max.d(2), max.q(5)  order = c(p(0), d(0), q(0)) 
seasonal_ar, seasonal_differences, seasonal_ma  max.P(2), max.D(1), max.Q(2)  seasonal = c(P(0), D(0), Q(0)) 
Other options can be set using set_engine()
.
auto_arima (default engine)
The engine uses forecast::auto.arima()
.
Function Parameters:
1 2 3 4 5 6 7 8 9  ## function (y, d = NA, D = NA, max.p = 5, max.q = 5, max.P = 2, max.Q = 2,
## max.order = 5, max.d = 2, max.D = 1, start.p = 2, start.q = 2, start.P = 1,
## start.Q = 1, stationary = FALSE, seasonal = TRUE, ic = c("aicc", "aic",
## "bic"), stepwise = TRUE, nmodels = 94, trace = FALSE, approximation = (length(x) >
## 150  frequency(x) > 12), method = NULL, truncate = NULL, xreg = NULL,
## test = c("kpss", "adf", "pp"), test.args = list(), seasonal.test = c("seas",
## "ocsb", "hegy", "ch"), seasonal.test.args = list(), allowdrift = TRUE,
## allowmean = TRUE, lambda = NULL, biasadj = FALSE, parallel = FALSE,
## num.cores = 2, x = y, ...)

The MAXIMUM nonseasonal ARIMA terms (max.p
, max.d
, max.q
) and
seasonal ARIMA terms (max.P
, max.D
, max.Q
) are provided to
forecast::auto.arima()
via arima_reg()
parameters.
Other options and argument can be set using set_engine()
.
Parameter Notes:
All values of nonseasonal pdq and seasonal PDQ are maximums.
The forecast::auto.arima()
model will select a value using these as an upper limit.
xreg
 This is supplied via the parsnip / modeltime fit()
interface
(so don't provide this manually). See Fit Details (below).
arima
The engine uses forecast::Arima()
.
Function Parameters:
1 2 3  ## function (y, order = c(0, 0, 0), seasonal = c(0, 0, 0), xreg = NULL, include.mean = TRUE,
## include.drift = FALSE, include.constant, lambda = model$lambda, biasadj = FALSE,
## method = c("CSSML", "ML", "CSS"), model = NULL, x = y, ...)

The nonseasonal ARIMA terms (order
) and seasonal ARIMA terms (seasonal
)
are provided to forecast::Arima()
via arima_reg()
parameters.
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).
method
 The default is set to "ML" (Maximum Likelihood).
This method is more robust at the expense of speed and possible
selections may fail unit root inversion testing. Alternatively, you can add method = "CSSML"
to
evaluate Conditional Sum of Squares for starting values, then Maximium Likelihood.
Date and DateTime Variable
It's a requirement to have a date or datetime variable as a predictor.
The fit()
interface accepts date and datetime features and handles them internally.
fit(y ~ date)
Seasonal Period Specification
The period can be nonseasonal (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 timebased 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 datetime feature. Simply use:
Formula Interface (recommended): fit(y ~ date)
will ignore xreg's.
XY Interface: fit_xy(x = data[,"date"], y = data$y)
will ignore xreg's.
Multivariate (xregs, Exogenous Regressors)
The xreg
parameter is populated using the fit()
or fit_xy()
function:
Only factor
, ordered factor
, and numeric
data will be used as xregs.
Date and Datetime variables are not used as xregs
character
data should be converted to factor.
Xreg Example: Suppose you have 3 features:
y
(target)
date
(time stamp),
month.lbl
(labeled month as a ordered factor).
The month.lbl
is an exogenous regressor that can be passed to the arima_reg()
using
fit()
:
fit(y ~ date + month.lbl)
will pass month.lbl
on as an exogenous regressor.
fit_xy(data[,c("date", "month.lbl")], y = data$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 datetime class values are excluded from xreg
.
fit.model_spec()
, set_engine()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  library(dplyr)
library(parsnip)
library(rsample)
library(timetk)
library(modeltime)
# Data
m750 < m4_monthly %>% filter(id == "M750")
m750
# Split Data 80/20
splits < initial_time_split(m750, prop = 0.8)
#  AUTO ARIMA 
# Model Spec
model_spec < arima_reg() %>%
set_engine("auto_arima")
# Fit Spec
model_fit < model_spec %>%
fit(log(value) ~ date, data = training(splits))
model_fit
#  STANDARD ARIMA 
# Model Spec
model_spec < arima_reg(
seasonal_period = 12,
non_seasonal_ar = 3,
non_seasonal_differences = 1,
non_seasonal_ma = 3,
seasonal_ar = 1,
seasonal_differences = 0,
seasonal_ma = 1
) %>%
set_engine("arima")
# Fit Spec
model_fit < model_spec %>%
fit(log(value) ~ date, data = training(splits))
model_fit

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