Making a function to build a database including inputsTest(denormalized), targetsTest, prediction values of 6 models,and the “timeDif” varialbe which means the time that daily maxinum occured(HORA.x) minus the time of inputs values happend(from 1 to 12) for 12 matrix

# predict the inputsTest values
prdReal <- function(model) {
    prd <- predict(model, newdata = inputsTest)
    return(prd)
}
library(RSNNS)
library(caretEnsemble)
library(pbapply)
# denoramilized inputsTest database into original data
timeDifTest <- function(inputs, hora) {
    load("NormData.RData")
    load("lmFit.RData")
    load("svmFit.RData")
    load("nnetFit.RData")
    load("rfFit.RData")
    load("linearFit.RData")
    load("greedyFit.RData")
    # denormalizeData
    deNormInputsTest <- denormalizeData(inputsTest, getNormParameters(inputsNorm))
    colnames(deNormInputsTest) <- colnames(inputs)
    # cbind the originial inputTest data,targetTest(nomiliazed data),and
    # predicton values of 6 models(nomilized data) together
    timeDifTestData <- cbind(deNormInputsTest, targetsTest, as.data.frame(prdReal(lmFit)))
    timeDifTestData <- cbind(timeDifTestData, as.data.frame(prdReal(svmFit)))
    timeDifTestData <- cbind(timeDifTestData, as.data.frame(prdReal(rfFit)))
    timeDifTestData <- cbind(timeDifTestData, as.data.frame(prdReal(nnetFit)))
    timeDifTestData <- cbind(timeDifTestData, as.data.frame(prdReal(greedyFit)))
    timeDifTestData <- cbind(timeDifTestData, as.data.frame(prdReal(linearFit)))
    a <- ncol(inputs)
    # add a new colum(timeDifTestData$HORA.x-hora),which is the clock that
    # maxmium happend minus the 1-12 oclock seperately
    timeDifTestData <- cbind(timeDifTestData, (timeDifTestData$HORA.x - hora))
    colnames(timeDifTestData)[(a + 1):(a + 8)] <- c("realO3", "lmFit", "svmFit", 
        "rfFit", "nnetFit", "greedyFit", "linearFit", "timeDif")
    save(timeDifTestData, file = "timeDifTestData.RData")
}