arima_boost: General Interface for "Boosted" ARIMA Regression Models

Description Usage Arguments Details Engine Details Fit Details See Also Examples

View source: R/parsnip-arima_boost.R

Description

arima_boost() is a way to generate a specification of a time series model that uses boosting to improve modeling errors (residuals) on Exogenous Regressors. It works with both "automated" ARIMA (auto.arima) and standard ARIMA (arima). The main algorithms are:

Usage

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arima_boost(
  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,
  mtry = NULL,
  trees = NULL,
  min_n = NULL,
  tree_depth = NULL,
  learn_rate = NULL,
  loss_reduction = NULL,
  sample_size = NULL,
  stop_iter = 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.

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.

mtry

A number for the number (or proportion) of predictors that will be randomly sampled at each split when creating the tree models (xgboost only).

trees

An integer for the number of trees contained in the ensemble.

min_n

An integer for the minimum number of data points in a node that is required for the node to be split further.

tree_depth

An integer for the maximum depth of the tree (i.e. number of splits) (xgboost only).

learn_rate

A number for the rate at which the boosting algorithm adapts from iteration-to-iteration (xgboost only).

loss_reduction

A number for the reduction in the loss function required to split further (xgboost only).

sample_size

number for the number (or proportion) of data that is exposed to the fitting routine.

stop_iter

The number of iterations without improvement before stopping (xgboost only).

Details

The data given to the function are not saved and are only used to determine the mode of the model. For arima_boost(), 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 ARIMA model are:

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

Model 1: ARIMA:

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))

Model 2: XGBoost:

modeltime xgboost::xgb.train
tree_depth max_depth (6)
trees nrounds (15)
learn_rate eta (0.3)
mtry colsample_bynode (1)
min_n min_child_weight (1)
loss_reduction gamma (0)
sample_size subsample (1)
stop_iter early_stop

Other options can be set using set_engine().

auto_arima_xgboost (default engine)

Model 1: Auto ARIMA (forecast::auto.arima):

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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, ...)

Parameter Notes:

Model 2: XGBoost (xgboost::xgb.train):

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## function (params = list(), data, nrounds, watchlist = list(), obj = NULL, 
##     feval = NULL, verbose = 1, print_every_n = 1L, early_stopping_rounds = NULL, 
##     maximize = NULL, save_period = NULL, save_name = "xgboost.model", xgb_model = NULL, 
##     callbacks = list(), ...)

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 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 (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_boost() 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(tidyverse)
library(lubridate)
library(parsnip)
library(rsample)
library(timetk)
library(modeltime)


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

# Split Data 80/20
splits <- initial_time_split(m750, prop = 0.9)

# MODEL SPEC ----

# Set engine and boosting parameters
model_spec <- arima_boost(

    # ARIMA args
    seasonal_period = 12,
    non_seasonal_ar = 0,
    non_seasonal_differences = 1,
    non_seasonal_ma = 1,
    seasonal_ar     = 0,
    seasonal_differences = 1,
    seasonal_ma     = 1,

    # XGBoost Args
    tree_depth = 6,
    learn_rate = 0.1
) %>%
    set_engine(engine = "arima_xgboost")

# FIT ----

## Not run: 
# Boosting - Happens by adding numeric date and month features
model_fit_boosted <- model_spec %>%
    fit(value ~ date + as.numeric(date) + month(date, label = TRUE),
        data = training(splits))

model_fit_boosted

## End(Not run)

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