time_series_forecasting_data_preprocessing.R

# DATA PREPRECESSING MODULE

cadairydata <- maml.mapInputPort(1)
# cadairydata  <- read.csv("cadairydata.csv", header = TRUE, stringsAsFactors = FALSE)

# Ensure the coding is consistent and convert column to a factor
cadairydata$Month <- as.factor(substr(cadairydata$Month, 1, 3))

# remove the first 2 columns
cadairydata <- cadairydata[, c(-1, -2)]  

# add column, for time series forescating, count months from the starting year
num.month <- function(Year, Month) {
  ## Find the starting year
  min.year  <- min(Year)

  ## Compute the number of months from the start of the time series
  12 * (Year - min.year) + Month - 1
}

cadairydata$Month.Count <- num.month(cadairydata$Year, cadairydata$Month.Number)

# value transformation with defensive programming
log.transform <- function(invec, multiplier = 1) {
  ## Function for the transformation, which is the log
  ## of the input value times a multiplier

  warningmessages <- c("ERROR: Non-numeric argument encountered in function log.transform",
                       "ERROR: Arguments to function log.transform must be greate than zero",
                       "ERROR: Aggurment multiplier to funcition log.transform must be a scaler",
                       "ERROR: Invalid time seies value encountered in function log.transform"
                       )

  ## Check the input arguments
  if(!is.numeric(invec) | !is.numeric(multiplier)) {warning(warningmessages[1]); return(NA)}  
  if(any(invec < 0.0) | any(multiplier < 0.0)) {warning(warningmessages[2]); return(NA)}
  if(length(multiplier) != 1) {{warning(warningmessages[3]); return(NA)}}

  ## Wrap the multiplication in tryCatch
  ## If there is an exception, print the warningmessage to
  ## standard error and return NA
  tryCatch(log(multiplier * invec),
           error = function(e){warning(warningmessages[4]); NA})
}


## Apply the transformation function to the 4 columns
## of the dataframe with production data
multipliers  <- list(1.0, 6.5, 1000.0, 1000.0)
cadairydata[, 4:7] <- Map(log.transform, cadairydata[, 4:7], multipliers)

## Get rid of any rows with NA values
cadairydata <- na.omit(cadairydata)  

maml.mapOutputPort('cadairydata')