ctree_fit: Determine model fit of CTREE tree

In ing-bank/tsforecast: Time Series Forecasting Pipeline

Description

ctree_fit A function to gauge the fit of a model run of an CTREE tree, given parameters. This is a function that is used to fine-tune the CTREE tree when forecasting

Usage

ctree_fit(
  ML_data,
  minsplit,
  mincriterion,
  minbucket,
  testtype = c("Bonferroni", "MonteCarlo", "Univariate", "Teststatistic"),
  teststat = c("quad", "max"),
  nresample = 9999
)

Arguments

ML_data	Dataset that has been prepared to run through CTREE. If originally a time series object, then it has gone through the decompose_ts_object_for_ML function and the first difference of the column of interest has been taken
minsplit	CTREE parameter. The minimum number of observations that must exist in a node in order for a split to be attempted (default from CTREE = 20)
mincriterion	CTREE parameter. The value of the test-statistic (testtype == "Teststatistic") or 1 - p-value that must be exceeded in order to implement a split (default from CTREE = 0.95)
minbucket	CTREE parameter. Minimum sum of weights in a terminal node (default from CTREE = 7)
testtype	CTREE parameter. Which distribution to use. Options are "Bonferroni", "MonteCarlo", "Univariate" and "Teststatistic"
teststat	CTREE parameter. Specifies which test statistic to use when doing hypothesis testing. Options are "quad" and "max"
nresample	CTREE parameter. Amount of resampling to do when MonteCarlo is selected as test type (default from CTREE = 9999)

Value

The mean absolute prediction error (MAPE), in percentage terms, of the model run

Examples

ML_data <- tstools::initialize_ts_forecast_data(
   data = dummy_gasprice, 
      date_col = "year_month", 
      col_of_interest = "gasprice", 
      group_cols = c("state", "oil_company"), 
      xreg_cols = c("spotprice", "gemprice")
   ) %>% 
   dplyr::filter(grouping == "state = New York   &   oil_company = CompanyA") %>% 
   tstools::transform_data_to_ts_object() %>% 
   decompose_ts_object_for_ML(filter_date_features = T) %>% 
   dplyr::mutate(col_of_interest = col_of_interest - dplyr::lag(col_of_interest)) %>% 
   dplyr::filter(!is.na(col_of_interest))
ctree_fit(
   ML_data = ML_data, 
   minsplit = 20, 
   mincriterion = 0.975, 
   minbucket = 5, 
   testtype = "Univariate", 
   teststat = "quad", 
   nresample = 9999
)

ing-bank/tsforecast documentation built on Sept. 18, 2020, 9:40 a.m.