General Interface for Bayesian Structural Time Series Models

bayesian_structural_reg() is a way to generate a specification of a Bayesian Structural Time Series Model before fitting and allows the model to be created using different packages. Currently the only package is bsts.

bayesian_structural_reg(mode = "regression", distribution = NULL)

Arguments

mode	A single character string for the type of model. The only possible value for this model is "regression".
distribution	The model family for the observation equation. Non-Gaussian model families use data augmentation to recover a conditionally Gaussian model.

Value

A model spec

Details

The data given to the function are not saved and are only used to determine the mode of the model. For bayesian_structural_reg(), the mode will always be "regression".

The model can be created using the fit() function using the following engines:

• "stan" (default) - Connects to bsts::bsts()

Main Arguments

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.

stan (default engine)

The engine uses bsts::bsts().

Parameter Notes:

• xreg - This is supplied via the parsnip / modeltime fit() interface (so don't provide this manually). See Fit Details (below).

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.

• fit(y ~ date)

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.

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 Date-time variables are not used as xregs

• character data should be converted to factor.

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():

• fit(y ~ date + month.lbl) will pass month.lbl on as an exogenous regressor.

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

See also

fit.model_spec(), set_engine()

Examples

if (FALSE) {
library(dplyr)
library(parsnip)
library(rsample)
library(timetk)
library(modeltime)
library(bayesmodels)

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

# Split Data 80/20
splits <- initial_time_split(m750, prop = 0.8)

ss <- AddLocalLinearTrend(list(), training(splits)$value)

# Model Spec
model_spec <- bayesian_structural_reg() %>%
    set_engine("stan", state.specification = ss)

# Fit Spec
model_fit <- model_spec %>%
    fit(log(value) ~ date, data = training(splits))
model_fit

predict(model_fit, testing(splits))

}