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General Interface for Temporal Hierarchical Forecasting (THIEF) Models

Source: R/parsnip-temporal_hierarchy.R
temporal_hierarchy.Rd

temporal_hierarchy() is a way to generate a specification of an Temporal Hierarchical Forecasting model before fitting and allows the model to be created using different packages. Currently the only package is thief. Note this function requires the thief package to be installed.

Usage

temporal_hierarchy(
  mode = "regression",
  seasonal_period = NULL,
  combination_method = NULL,
  use_model = 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.

combination_method

Combination method of temporal hierarchies, taking one of the following values:

  • "struc" - Structural scaling: weights from temporal hierarchy

  • "mse" - Variance scaling: weights from in-sample MSE

  • "ols" - Unscaled OLS combination weights

  • "bu" - Bottom-up combination – i.e., all aggregate forecasts are ignored.

  • "shr" - GLS using a shrinkage (to block diagonal) estimate of residuals

  • "sam" - GLS using sample covariance matrix of residuals

use_model

Model used for forecasting each aggregation level:

  • "ets" - exponential smoothing

  • "arima" - arima

  • "theta" - theta

  • "naive" - random walk forecasts

  • "snaive" - seasonal naive forecasts, based on the last year of observed data

Details

Models can be created using the following engines:

Engine Details

The standardized parameter names in modeltime can be mapped to their original names in each engine:

modeltimethief::thief()
combination_methodcomb
use_modelusemodel

Other options can be set using set_engine().

thief (default engine)

The engine uses thief::thief().

Function Parameters:

#> function (y, m = frequency(y), h = m * 2, comb = c("struc", "mse", "ols", 
#>     "bu", "shr", "sam"), usemodel = c("ets", "arima", "theta", "naive", 
#>     "snaive"), forecastfunction = NULL, aggregatelist = NULL, ...)

Other options and argument can be set using set_engine().

Parameter Notes:

  • xreg - This model is not set up to use exogenous regressors. Only univariate models will be fit.

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:

For univariate analysis, you must include a date or date-time 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)

This model is not set up for use with exogenous regressors.

References

  • For forecasting with temporal hierarchies see: Athanasopoulos G., Hyndman R.J., Kourentzes N., Petropoulos F. (2017) Forecasting with Temporal Hierarchies. European Journal of Operational research, 262(1), 60-74.

  • For combination operators see: Kourentzes N., Barrow B.K., Crone S.F. (2014) Neural network ensemble operators for time series forecasting. Expert Systems with Applications, 41(9), 4235-4244.

See also

fit.model_spec(), set_engine()

Examples

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

# 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)

# ---- HIERARCHICAL ----

# Model Spec - The default parameters are all set
# to "auto" if none are provided
model_spec <- temporal_hierarchy() %>%
    set_engine("thief")

# Fit Spec
model_fit <- model_spec %>%
    fit(log(value) ~ date, data = training(splits))
#> frequency = 12 observations per 1 year
model_fit
#> parsnip model object
#> 
#>         Jan      Feb      Mar      Apr      May      Jun      Jul      Aug
#> 21                                     9.292876 9.278808 9.177530 9.161701
#> 22 9.292288 9.290665 9.310183 9.314852 9.318892 9.304824 9.203573 9.187635
#> 23 9.318275 9.316547 9.336091 9.340759                                    
#>         Sep      Oct      Nov      Dec
#> 21 9.210732 9.279095 9.292842 9.296845
#> 22 9.236714 9.305078 9.318782 9.322784
#> 23