tspred function

Time series prediction process

Constructor for the tspred class representing a time series prediction process. This process may involve subsetting the time series data into training and testing sets, preprocessing/postprocessing the data, modeling, prediction and finally an evaluation of modeling fitness and prediction quality. All these process steps should be based on particular time series transformation methods, a modeling and prediction method, and quality metrics which are defined in a tspred class object.

tspred(
  subsetting = NULL,
  processing = NULL,
  modeling = NULL,
  evaluating = NULL,
  data = NULL,
  n.ahead = NULL,
  one_step = FALSE,
  ...,
  subclass = NULL
)

Arguments

• subsetting: A subsetting object regarding subsetting processing.
• processing: List of named processing objects used for pre(post)processing the data.
• modeling: A modeling object used for time series modeling and prediction.
• evaluating: List of named evaluating objects used for prediction/modeling quality evaluation.
• data: A list of time series to be pre(post)processed, modelled and/or predicted.
• n.ahead: Integer defining the number of observations to be predicted.
• one_step: Should the function produce one-step ahead predictions? If FALSE, a multi-step ahead prediction approach is adopted.
• ...: Other parameters to be encapsulated in the class object.
• subclass: Name of new specialized subclass object created in case it is provided.

Returns

An object of class tspred.

Examples

#Obtaining objects of the processing class
  proc1 <- subsetting(test_len=20)
  proc2 <- BoxCoxT(lambda=NULL)
  proc3 <- WT(level=1, filter="bl14")

  #Obtaining objects of the modeling class
  modl1 <- ARIMA()

  #Obtaining objects of the evaluating class
  eval1 <- MSE_eval()
  eval2 <- MAPE_eval()

  #Defining a time series prediction process
  tspred_1 <- tspred(subsetting=proc1,
                     processing=list(BCT=proc2,
                                     WT=proc3),
                     modeling=modl1,
                     evaluating=list(MSE=eval1,
                                     MAPE=eval2)
                    )
  summary(tspred_1)

         #Obtaining objects of the processing class
  proc4 <- SW(window_len = 6)
  proc5 <- MinMax()

  #Obtaining objects of the modeling class
  modl2 <- NNET(size=5,sw=proc4,proc=list(MM=proc5))

  #Defining a time series prediction process
  tspred_2 <- tspred(subsetting=proc1,
                     processing=list(BCT=proc2,
                                     WT=proc3),
                     modeling=modl2,
                     evaluating=list(MSE=eval1,
                                     MAPE=eval2)
                    )
  summary(tspred_2)

See Also

Other constructors: ARIMA(), LT(), MSE_eval(), evaluating(), modeling(), processing()

Author(s)

Rebecca Pontes Salles