In neerajdhanraj/PredictTestbench: Test Bench for Comparison of Data Prediction Models

This Document is to introduce the R package 'PredictTestbench'. It is an testing workbench for comparison of time series data prediction models/methods. It compares prediction methods with reference to RMSE, MAE or MAPE parameters. It allows to add new proposed methods to test bench and to compare with other methods. The function prediction_append() allows to add multiple numbers of methods to the existing methods available in test bench.

Following example describs the working of this package:

Consider a sample data datax as follows:

datax <- c(1:5,1:5,1:5,1:5,1:5,1:5,1:5,1:5,1:5,1:5,1:5,1:5,1:5,1:5,1:5,1:5,1:5,1:5,1:5,1:5,1:5)

Import library for Package PredictTestbench as follows:

library(PredictTestbench)

The function prediction_errors() is used to compare imputing methods with reference to RMSE, MAE or MAPE parameters. Syntax of prediction_errors() as shown below:

prediction_errors(dataIn, nextVal, errorParameter, MethodPath, MethodName)

where,

• dataIn is imput time series for testing
• nextVal is an integer to decide number of values to predict
• errorParameter is type of error calculation (RMSE, MAE or MAPE)
• MethodPath is location of function for the proposed imputation method
• MethodName is name for function for the proposed imputation method

At simplest form, function prediction_errors() can we used as:

q <- prediction_errors(datax, nextVal = 10)
q
plot_predictions(q)

By default, this function compares two prediction methods, i.e. ARIMA and PSF https://cran.r-project.org/package=PSF. The plot_predictions() function is used to plot the comparison plots between different methods. This testbench allows to add one more data prediction method to compare with already existing methods. The only care is to be takes as, the new imputing method is to be designed in function format such that it could return predicted data as output. Suppose, following function is the desired method to add in testbench.

===============================

inter <- function(dataIn)

{

library(PSF)

d <- pred_for_w(dataIn, 4, 3, 10)

return(d)

}

===============================

Save this function in new R script file and save it and note its Source location similar to "source('~/PredictTestbench/R/inter.R')" and use prediction_errors() function as:

#aa <- prediction_errors(dataIn= datax, nextVal = 10, MethodPath = "source('~/PredictTestbench/R/inter.R')", MethodName = "Proposed_Method")

#aa
#plot_predictions(aa)

This above code is written in commented format, since this function is dependent on other function and its location, which is not included in this package.

If user wishes to add more than one imputation methods to test bench, the function prediction_append() is used as:

#bb <- prediction_append(existing_method = aa, dataIn= datax, nextVal = 10, MethodPath = "source('~/imputeTestbench/R/PSFimpute.R')", MethodName = "PSFimpute")

#bb
#plot_predictions(bb)

where

• existing_method is output obtained from prediction_errors() function
• dataIn is imput time series for testing
• nextVal is an integer to decide number of values to predict
• errorParameter is type of error calculation (RMSE, MAE or MAPE)
• MethodPath is location of function for the proposed imputation method
• MethodName is name for function for the proposed imputation method

In similar way, user is allowed to remove unwanted prediction method in testbench, if any. This is done with prediction_remove() function.

# cc <- prediction_remove (existing_method = bb, index_number = 1)
# cc
# plot_predictions(cc)

where

• existing_method is Error observations for different methods
• index_number is index number of unwanted method in study

One/two Step ahead forecasting is done with function

#a1 <- step_ahead_forecast(dataIn = datax, trainedData = 80, MethodPath = "source('~/imputeTestbench/R/PSFimpute.R')", errorParameter = 3, stepSize = 1)
#a1

neerajdhanraj/PredictTestbench documentation built on May 23, 2019, 1:31 p.m.