arima_reg: General Interface for ARIMA Regression Models

Description Usage Arguments Details Engine Details Fit Details See Also Examples

View source: R/parsnip-arima_reg.R

Description

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.

Usage

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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.

non_seasonal_ar

The order of the non-seasonal auto-regressive (AR) terms. Often denoted "p" in pdq-notation.

non_seasonal_differences

The order of integration for non-seasonal differencing. Often denoted "d" in pdq-notation.

non_seasonal_ma

The order of the non-seasonal moving average (MA) terms. Often denoted "q" in pdq-notation.

seasonal_ar

The order of the seasonal auto-regressive (SAR) terms. Often denoted "P" in PDQ-notation.

seasonal_differences

The order of integration for seasonal differencing. Often denoted "D" in PDQ-notation.

seasonal_ma

The order of the seasonal moving average (SMA) terms. Often denoted "Q" in PDQ-notation.

Details

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:

Main Arguments

The main arguments (tuning parameters) for the model are:

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.

Engine Details

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:

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## 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:

arima

The engine uses forecast::Arima().

Function Parameters:

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## 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("CSS-ML", "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:

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 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:

  1. seasonal_period = "auto": A seasonal 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 (No xregs, Exogenous Regressors):

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

Multivariate (xregs, Exogenous Regressors)

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

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():

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

See Also

fit.model_spec(), set_engine()

Examples

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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

modeltime documentation built on June 13, 2021, 5:06 p.m.