transForecast_ann: Forecast - using Artificial Neural Networks
In RTransProb: Analyze and Forecast Credit Migrations

Description Usage Arguments Value Author(s) Examples

This model implements a forecasting method using Artificial Neural Networks.

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transForecast_ann(data, histData, predData_ann, startDate, endDate,
                    method, interval, snapshots, defind, depVar, indVars,  ratingCat, 
                    pct, hiddenlayers,activation,stepMax,rept, calibration)

`data`	a table containing historical credit ratings data (i.e., credit migration data). A dataframe of size nRecords x 3 where each row contains an ID (column 1), a date (column 2), and a credit rating (column 3); The credit rating is the rating assigned to the corresponding ID on the corresponding date.
`histData`	historical macroeconomic,financial and non-financial data.
`predData_ann`	forecasting data.
`startDate`	start date of the estimation time window, in string or numeric format.
`endDate`	end date of the estimation time window, in string or numeric format.
`method`	estimation algorithm, in string format. Valid values are 'duration' or 'cohort'.
`interval`	the length of the transition interval under consideration, in years. The default value is 1, i.e., 1-year transition probabilities are estimated.
`snapshots`	integer indicating the number of credit-rating snapshots per year to be considered for the estimation. Valid values are 1, 4, or 12. The default value is 1, i.e., one snapshot per year. This parameter is only used in the 'cohort' algorithm.
`defind`	Default Indicator
`depVar`	dependent variable, as a string.
`indVars`	list containing the independent variables.
`ratingCat`	list containing the unique rating categories
`pct`	percent of data used in training dataset.
`hiddenlayers`	a vector of integers specifying the number of hidden neurons (vertices) in each layer.
`activation`	activation function. strings, 'logistic' and 'tanh' are possible for the logistic function and tangent hyperbolicus
`stepMax`	the maximum steps for the training of the neural network. Reaching this maximum leads to a stop of the neural network's training process.
`rept`	the number of repetitions for the neural network's training.
`calibration`	determines if code uses the caret package to find optimal parameter. 'Yes' and 'No'

The output consists of a forecasted transition matrix using ANN.

Abdoulaye (Ab) N'Diaye

## Not run: 
library(dplyr)
library(plyr)
library(Matrix)


for (i in c(24,25,26)) {
 tic()
 data <- data
 
 histData <- histData.normz

 predData_ann2 <- predData_ann_Baseline
 predData_ann2 <- subset(
   predData_ann2,
   X == i,
   select = c(Market.Volatility.Index..Level..normz

   )
 )
 
 indVars   = c("Market.Volatility.Index..Level..normz"

 )
 
 
 startDate = "1991-08-16"
 endDate   = "2007-08-16"
 
 
 depVar <- c("end_rating")
 
 pct <- 1
 wgt <-  "mCount"
 ratingCat <- c("A", "B", "C", "D", "E", "F", "G")
 defind    <- "G"
 
 
 ratingCat <- as.numeric(factor(
   ratingCat,
   levels = c('A', 'B', 'C', 'D', 'E', 'F', 'G'),
   labels = c(1, 2, 3, 4, 5, 6, 7)
 ))
 
 defind <- as.numeric(factor(
   defind,
   levels = c('A', 'B', 'C', 'D', 'E', 'F', 'G'),
   labels = c(1, 2, 3, 4, 5, 6, 7)
 ))
 
 method    = "cohort"
 snapshots = 1
 interval  = 1
 hiddenlayers = c(1)
 activation = "logistic"
 stepMax = 1e9                #increase to make sure the DNN converges
 calibration = "FALSE"
 rept = 1
 
 ann_TM <-
   transForecast_ann(
     data,
     histData,
     predData_ann2,
     startDate,
     endDate,
     method,
     interval,
     snapshots,
     defind,
     depVar,
     indVars,
     ratingCat,
     pct,
     hiddenlayers,
     activation,
     stepMax,
     rept,
     calibration
   )
 print(ann_TM)
 
 toc()
}

## End(Not run)