D Predictive Modelling

What-If analysis reports use the reporting line forecast values that are generated using the Arima Algorithm in the R code seeded with the application. R has a base package called 'stats' which provides the function called 'arima()'. This function enables the usage of the ARIMA technique for time series forecasting.

The guidelines associated with respect to the execution R code are mentioned below:

• Data should be generated for at least one group for more than 12 continuous MIS dates. 12 is the parameter n Consider where we are setting how many records are significant to be considered for prediction.
• The assumption is that the data is chronological for consecutive end of month dates. By default, the prediction will be done for 60 months starting with the immediate month after the last available MIS Date.
• If the data provided is not for the chronological end of month dates, results generated will not be accurate.
• ARIMA is a statistical technique used for time-series predictions. It accepts a host of parameters of which the basic parameters are p,d, and q. p is the order of Autoregressive Process, q is the parameter for the Moving Average process and d is the number of differences after which the data can be considered stationary with the desired confidence level. It has more parameters that can be customized.
• Detailed documentation of the technique can be found at:

  http://www.dms.umontreal.ca/~duchesne/chap7.pdf

  Documentation of implementation of ARIMA in R can be found at:

  http://stat.ethz.ch/R-manual/R-patched/library/stats/html/arima.html

Logging for the individual groups' ARIMA model summaries will happen in a file named out.log. This log file will be generated in the $FIC_DB_HOME/bin folder. If a particular group has unstable data and prediction fails, the corresponding error will also be documented in the out.log file against that particular group.