Random forest gdp prediction code

//GDP forecasting using Random Forests
new;
library gml,tsmt;
 
/****************************************
Load data from GDP dataset
****************************************/
//GDP data quarterly
path = "C:/svn/apps/gml/examples/gdp_tutorial";
gdp_q = loadd(path $+ "/rf_gdp.dat", "rgdp_pc");
 
//Load all other features
features_q = loadd(path $+ "/rf_gdp.dat", ". -rgdp_pc" );
 
//Load variable names
vnames = getHeaders(path $+ "/rf_gdp.dat");
 
/****************************************
Split data for training and testing
Testing : 1961Q2 to 1999Q4
Training : 2000Q1 to 2017Q4
*****************************************/
testT = 155; 
 
//Print date ranges
print "Start data of test data:" dttostr(features_q[1,1], "YYYY-QQ");
print "End date of test data:" dttostr(features_q[testT,1], "YYYY-QQ");
print "Start date of training data:" dttostr(features_q[testT+1,1], "YYYY-QQ");
print "End date of training data:" dttostr(features_q[rows(features_q),1], "YYYY-QQ");
 
/***********************************************
Set up data for random forest predictions
For training we will use through observation
************************************************/
y_train = gdp_q[1:testT,.];
x_train = features_q[1:testT,2:45];
y_test = gdp_q[testT+1:rows(gdp_q),.];
x_test = features_q[testT+1:rows(features_q),2:45];
 
/**********************************************
Set up parameters for fitting model
***********************************************/
//Use control structure for settings
struct rfControl rfc;
rfc = rfControlCreate;
 
//Turn on variable importance
rfc.variableImportanceMethod = 2;
 
//Turn on OOB error
rfc.oobError = 1;
 
/**********************************
Fit model
**********************************/
//Output structure
struct rfModel out;
 
//Fit training data using random forest
out = rfRegressFit(y_train, x_train, rfc);
 
//OOB Error
print "Out-of-bag error:" out.oobError;
 
/*************************************
Plot variable importance
**************************************/
plotVariableImportance(out, vnames[3:46]);
 
/**********************************
Predictions
**********************************/
//Make predictions using test data
predictions = rfRegressPredict(out, x_test);
 
//Print predictions
print predictions[1:10]~y_test[1:10];
print "random forest MSE: " meanc((predictions - y_test).^2);
 
////Print ols MSE
b_hat = y_train/(ones(rows(x_train), 1)~x_train);
y_hat = (ones(rows(x_test),1)~x_test) * b_hat;
print "OLS MSE using test data  : " meanc((y_hat - y_test).^2);
corrx( predictions~y_test);
 
/*****************************************
Plot GDP data using plotTS
******************************************/
//Set Canvas Size
plotOpenWindow;
 
//start date
dtstart = features_q[1,1];
 
//Plot control structure
struct plotControl myPlot;
myPlot = plotGetDefaults("XY");
 
//Place first 'X' tic mark at 1984 month 1 and draw one every 6 months
plotSetXTicInterval(&myPlot, 20, 1961);
 
//Display only 4 digit year on 'X' tic labels
plotSetXTicLabel(&myPlot, "YYYY-QQ");
 
//Legend
plotSetLegend(&myPlot, "Obs."$|"Predicted","TOP RIGHT", 1);
 
//Plot title
plotSetTitle(&myPlot, "U.S. Real GDP (Annual Percent Change, 2009=100)");
plotTS(myPlot, dtstart, 4, gdp_q*100);
 
//Add predictions
plotAddTS(features_q[testT+1,1], 4, predictions*100);