In josephsdavid/tswgewrapped: Helpful wrappers for 'tswge', 'vars' and 'nnfor' time series packages

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

The objective of this vignette is to show how to use the ModelCompareUnivariate object to compare multiple potential models.

Setup Libraries

library(tswgewrapped)
library(tswge)

Load Data

data("airlog")

Setup Models to be compared

# Woodward Gray Airline Model
phi_wg = c(-0.36, -0.05, -0.14, -0.11, 0.04, 0.09, -0.02, 0.02, 0.17, 0.03, -0.10, -0.38)
d_wg = 1
s_wg = 12

# Parzen Model
phi_pz = c(0.74, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.38, -0.2812)
s_pz = 12

# Box Model
d_bx = 1
s_bx = 12  
theta_bx =  c(0.40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.60, -0.24)

models = list("Woodward Gray Model A" = list(phi = phi_wg, d = d_wg, s = s_wg, vara = 0.001357009, sliding_ase = FALSE),
              "Woodward Gray Model B" = list(phi = phi_wg, d = d_wg, s = s_wg, vara = 0.001357009, sliding_ase = TRUE),
              "Parzen Model A" = list(phi = phi_pz, s = s_pz, vara = 0.002933592, sliding_ase = FALSE),
              "Parzen Model B" = list(phi = phi_pz, s = s_pz, vara = 0.002933592, sliding_ase = TRUE),
              "Box Model A" = list(theta = theta_bx, d = d_bx, s = s_bx, vara = 0.001391604, sliding_ase = FALSE),
              "Box Model B" = list(theta = theta_bx, d = d_bx, s = s_bx, vara = 0.001391604, sliding_ase = TRUE)
              )

Initialize the ModelCompareUnivariate object

#### With n_step.ahead = TRUE (Default)              
mdl_compare = ModelCompareUnivariate$new(data = airlog, mdl_list = models,
                                         n.ahead = 36, batch_size = 72)

Compare the models

Plot Multiple realizations

p = mdl_compare$plot_multiple_realizations(n.realizations = 4, seed = 100, plot = "realization", scales = 'fixed')

p = mdl_compare$plot_multiple_realizations(n.realizations = 4, seed = 100, plot = c("acf", "spectrum"), scales = 'fixed')

Compare boxplot of ASE values

p = mdl_compare$plot_boxplot_ases()

Statistically Compare the models

mdl_compare$statistical_compare()  

Simple Forecasts (with various options)

p = mdl_compare$plot_simple_forecasts(lastn = TRUE, limits = FALSE)
p = mdl_compare$plot_simple_forecasts(lastn = TRUE, limits = TRUE)
p = mdl_compare$plot_simple_forecasts(lastn = FALSE, limits = FALSE)
p = mdl_compare$plot_simple_forecasts(lastn = FALSE, limits = TRUE)
p = mdl_compare$plot_simple_forecasts(zoom = 50)  ## Zoom into plot

Plot and compare the forecasts per batch

p = mdl_compare$plot_batch_forecasts(only_sliding = TRUE)

Raw Data and Metrics

ASEs = mdl_compare$get_tabular_metrics(ases = TRUE)
print(ASEs)

forecasts = mdl_compare$get_tabular_metrics(ases = FALSE)
print(forecasts)

Advanced Options

Adding models after object initialization

## Add initial set of models (without sliding ASE)
models = list("Woodward Gray Model A" = list(phi = phi_wg, d = d_wg, s = s_wg, vara = 0.001357009, sliding_ase = FALSE),
              "Parzen Model A" = list(phi = phi_pz, s = s_pz, vara = 0.002933592, sliding_ase = FALSE),
              "Box Model A" = list(theta = theta_bx, d = d_bx, s = s_bx, vara = 0.001391604, sliding_ase = FALSE)
              )

mdl_compare = ModelCompareUnivariate$new(data = airlog, mdl_list = models,
                                         n.ahead = 36)

# Following will give an error because we are trying to add models with the same name again
# mdl_compare$add_models(mdl_list = models)

## Add models with Sliding ASE
models = list("Woodward Gray Model B" = list(phi = phi_wg, d = d_wg, s = s_wg, vara = 0.001357009, sliding_ase = TRUE),
              "Parzen Model B" = list(phi = phi_pz, s = s_pz, vara = 0.002933592, sliding_ase = TRUE),
              "Box Model B" = list(theta = theta_bx, d = d_bx, s = s_bx, vara = 0.001391604, sliding_ase = TRUE)
             )

# This will give warning for batch size which has not been set.
# Internally it will be set to use just 1 batch since nothing has been specified.
mdl_compare$add_models(mdl_list = models)  # This will give a warning and set only 1 batch
# mdl_compare$add_models(mdl_list = models, batch_size = 72)  ## This will not give a warning

Adjusting Batch Size

# If you had forgotten to set the batch size, you can do it after the fact  
mdl_compare$set_batch_size(72)


# Check all functions after data manipulation
p = mdl_compare$plot_simple_forecasts(zoom = 72)
p = mdl_compare$plot_batch_forecasts(only_sliding = TRUE)
p = mdl_compare$plot_batch_ases(only_sliding = TRUE)
p = mdl_compare$plot_boxplot_ases()
mdl_compare$statistical_compare()

Removing Unwanted Models

## After comparison, maybe you want to remove some models that are not performing well or you do not want to show in your plots
mdl_names = c("Woodward Gray Model A", "Parzen Model A", "Box Model A", "Parzen Model B")

# Remove models
mdl_compare$remove_models(mdl_names = mdl_names)

# Check all functions after data manipulation
p = mdl_compare$plot_simple_forecasts()
p = mdl_compare$plot_batch_forecasts()
p = mdl_compare$plot_batch_ases()
p = mdl_compare$plot_boxplot_ases()
mdl_compare$statistical_compare()

josephsdavid/tswgewrapped documentation built on July 31, 2020, 9:36 a.m.