Teradata Package for R Function Reference | 17.00 - ArimaPredict - Teradata Package for R - Look here for syntax, methods and examples for the functions included in the Teradata Package for R.

ARIMAPredict

Description

The ARIMAPredict function takes as input the ARIMA model produced by the ARIMA (td_arima_mle) function and predicts a specified number of future values (time point forecasts) for the modeled sequence.

Usage

  td_arima_predict_mle (
      object = NULL,
      residual.table = NULL,
      n.ahead = 1,
      partition.columns = NULL,
      object.sequence.column = NULL,
      residual.table.sequence.column = NULL,
      object.partition.column = NULL,
      residual.table.partition.column = NULL,
      object.order.column = NULL,
      residual.table.order.column = NULL
  )

Arguments

object	Required Argument. Specifies the model tbl_teradata generated by td_arima_mle. This argument can accept either a tbl_teradata or an object of "td_arima_mle" class.
object.partition.column	Required Argument. Specifies Partition By columns for "object". Values to this argument can be provided as a vector, if multiple columns are used for partition. Types: character OR vector of Strings (character)
object.order.column	Optional Argument. Specifies Order By columns for "object". Values to this argument can be provided as a vector, if multiple columns are used for ordering. Types: character OR vector of Strings (character)
residual.table	Required Argument. Specifies the name of the tbl_teradata that contains the original input parameters and their residuals. This tbl_teradata is the residual tbl_teradata generated by the td_arima_mle function.
residual.table.partition.column	Required Argument. Specifies Partition By columns for "residual.table". Values to this argument can be provided as a vector, if multiple columns are used for partition. Types: character OR vector of Strings (character)
residual.table.order.column	Required Argument. Specifies Order By columns for "residual.table". Values to this argument can be provided as a vector, if multiple columns are used for ordering. Types: character OR vector of Strings (character)
n.ahead	Required Argument. Specifies the number of steps to forecast after the end of the time series. This value must be a positive integer. Default Value: 1 Types: integer
partition.columns	Optional Argument. Required if tdplyr is connected to Vantage 1.1.1 or earlier versions. Specifies the partition columns that are in model tbl_teradata and residual tbl_teradata. Types: character OR vector of Strings (character)
object.sequence.column	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "object". The argument is used to ensure deterministic results for functions which produce results that vary from run to run. Types: character OR vector of Strings (character)
residual.table.sequence.column	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "residual.table". The argument is used to ensure deterministic results for functions which produce results that vary from run to run. Types: character OR vector of Strings (character)

Value

Function returns an object of class "td_arima_predict_mle" which is a named list containing object of class "tbl_teradata".
Named list member can be referenced directly with the "$" operator using the name: result.

Examples

  
    # Get the current context/connection
    con <- td_get_context()$connection
    
    # Load example data.
    loadExampleData("arima_example", "milk_timeseries")

    # Create object(s) of class "tbl_teradata".
    milk_timeseries <- tbl(con, "milk_timeseries")

    # Generate arima model using "orders" parameter.
    td_arima_out <- td_arima_mle(data = milk_timeseries,
                                 timestamp.columns = "period",
                                 value.column = "milkpound",
                                 orders = "3,0,0",
                                 include.mean = TRUE
                                 )

    # Example 1: Using the generated Arima model to find predictions.
    td_arima_predict_out <- td_arima_predict_mle(td_arima_out,
                                          object.partition.column='td_arima_partition_id',
                                          residual.table=td_arima_out$residual.table,
                                          residual.table.partition.column='td_arima_partition_id',
                                          residual.table.order.column='period',
                                          partition.columns='td_arima_partition_id',
                                          n.ahead=15
                                          )

     # Example 2: Alternatively use the predict S3 method to find predictions.
     predict_out <- predict(td_arima_out,
                            object.partition.column='td_arima_partition_id',
                            residual.table=td_arima_out$residual.table,
                            residual.table.partition.column='td_arima_partition_id',
                            residual.table.order.column='period',
                            partition.columns='td_arima_partition_id',
                            n.ahead=15
                            )

     # Example 3: Using coefficient tbl_teradata from a presisted Arima model generated 
     # by td_arima_mle() function.
     # Persist the models generated by td_arima_mle() function.
     copy_to(con, df = td_arima_out$coefficient, name = "test_td_arima_coefficients", 
             overwrite = TRUE)
     copy_to(con, df = td_arima_out$residual.table, name = "test_td_arima_residualtable", 
             overwrite = TRUE)

     # Create object(s) of class "tbl_teradata".
     arima_coefficients <- tbl(con, "test_td_arima_coefficients")
     arima_residualtable <- tbl(con, "test_td_arima_residualtable")

     # Find prediction.
     td_arima_predict_out <- td_arima_predict_mle(object=arima_coefficients,
                                          object.partition.column='td_arima_partition_id',
                                          residual.table=arima_residualtable,
                                          residual.table.partition.column='td_arima_partition_id',
                                          residual.table.order.column='period',
                                          partition.columns='td_arima_partition_id',
                                          n.ahead=15
                                          )