General Interface for NAIVE Forecast Models

naive_reg() is a way to generate a specification of an NAIVE or SNAIVE model before fitting and allows the model to be created using different packages.

Usage

naive_reg(mode = "regression", id = NULL, seasonal_period = NULL)

Arguments

mode: A single character string for the type of model. The only possible value for this model is "regression".
id: An optional quoted column name (e.g. "id") for identifying multiple time series (i.e. panel data).
seasonal_period: SNAIVE only. 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.

Details

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

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

"naive" (default) - Performs a NAIVE forecast
"snaive" - Performs a Seasonal NAIVE forecast

Engine Details

naive (default engine)

The engine uses naive_fit_impl()
The NAIVE implementation uses the last observation and forecasts this value forward.
The id can be used to distinguish multiple time series contained in the data
The seasonal_period is not used but provided for consistency with the SNAIVE implementation

snaive (default engine)

The engine uses snaive_fit_impl()
The SNAIVE implementation uses the last seasonal series in the data and forecasts this sequence of observations forward
The id can be used to distinguish multiple time series contained in the data
The seasonal_period is used to determine how far back to define the repeated series. This can be a numeric value (e.g. 28) or a period (e.g. "1 month")

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)

ID features (Multiple Time Series, Panel Data)

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

ID Example: Suppose you have 3 features:

y (target)
date (time stamp),
series_id (a unique identifer that identifies each time series in your data).

The series_id can be passed to the naive_reg() using fit():

naive_reg(id = "series_id") specifes that the series_id column should be used to identify each time series.
fit(y ~ date + series_id) will pass series_id on to the underlying naive or snaive functions.

Seasonal Period Specification (snaive)

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.

External Regressors (Xregs)

These models are univariate. No xregs are used in the modeling process.

Examples

library(dplyr)
library(parsnip)
library(rsample)
library(timetk)

# Data
m750 <- m4_monthly %>% filter(id == "M750")
m750
#> # A tibble: 306 × 3
#>    id    date       value
#>    <fct> <date>     <dbl>
#>  1 M750  1990-01-01  6370
#>  2 M750  1990-02-01  6430
#>  3 M750  1990-03-01  6520
#>  4 M750  1990-04-01  6580
#>  5 M750  1990-05-01  6620
#>  6 M750  1990-06-01  6690
#>  7 M750  1990-07-01  6000
#>  8 M750  1990-08-01  5450
#>  9 M750  1990-09-01  6480
#> 10 M750  1990-10-01  6820
#> # ℹ 296 more rows

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

# ---- NAIVE ----

# Model Spec
model_spec <- naive_reg() %>%
    set_engine("naive")

# Fit Spec
model_fit <- model_spec %>%
    fit(log(value) ~ date, data = training(splits))
model_fit
#> parsnip model object
#> 
#> NAIVE
#> --------
#> Model: 
#> # A tibble: 1 × 2
#>   date       value
#>   <date>     <dbl>
#> 1 2010-04-01  9.29


# ---- SEASONAL NAIVE ----

# Model Spec
model_spec <- naive_reg(
        id = "id",
        seasonal_period = 12
    ) %>%
    set_engine("snaive")

# Fit Spec
model_fit <- model_spec %>%
    fit(log(value) ~ date + id, data = training(splits))
model_fit
#> parsnip model object
#> 
#> SNAIVE [12]
#> --------
#> Model: 
#> # A tibble: 12 × 3
#>    id    date       value
#>    <fct> <date>     <dbl>
#>  1 M750  2009-05-01  9.27
#>  2 M750  2009-06-01  9.27
#>  3 M750  2009-07-01  9.15
#>  4 M750  2009-08-01  9.19
#>  5 M750  2009-09-01  9.18
#>  6 M750  2009-10-01  9.25
#>  7 M750  2009-11-01  9.26
#>  8 M750  2009-12-01  9.27
#>  9 M750  2010-01-01  9.26
#> 10 M750  2010-02-01  9.26
#> 11 M750  2010-03-01  9.29
#> 12 M750  2010-04-01  9.29