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);

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