threefeather

hyper_params <- list(
        activation=c("Rectifier","Tanh","Maxout","RectifierWithDropout","TanhWithDropout","MaxoutWithDropout"),
        hidden=list(c(200,200),c(500,500),c(500,500,500)),
        input_dropout_ratio=c(0,0.05,0.1,0.2),
        l1=seq(0,1e-4,1e-6),
        l2=seq(0,1e-4,1e-6)
)

threefeather

model.class<-multinom(IR~ .,data = Ist.class.2)
Call:
multinom(formula = IR ~ ., data = Ist.class.2)

Coefficients:
  (Intercept) unem.rate   BankLoan RealExport Personal.Consumption.Expenditures      RGDP Total.Vehicle.Sales U.S.Dollar.Index
0   -2.452802 0.5361296 -0.1061213 0.04649329                        -0.2893107 0.1903935           0.3060163      -0.05124506
1   -6.171310 0.2994286  1.0046571 0.24750510                         0.2660045 0.1271559           0.3456129      -0.02169250

Std. Errors:

threefeather

randomForest(formula = IR ~ ., data = Ist.month.class.mc, importance = TRUE,      subset = train.mc) 
               Type of random forest: classification
                     Number of trees: 500
No. of variables tried at each split: 2

        OOB estimate of  error rate: 26.25%
Confusion matrix:
   -1   0  1 class.error
-1 21  54  1   0.7236842
0  17 362 25   0.1039604

threefeather

###try to predict the outliers
abm<-Ist[c(373,622,667,706,709),-c(1,4)]
abm.pre<-predict(model.season_final.re,abm)
data.frame(Date=Ist$Date[c(373,622,667,706,709)],Prediction=abm.pre, Real=Ist$Interest.Rate[c(373,622,667,706,709)])

          Date Prediction  Real
373 1981-01-01   7.250878 13.00
622 2001-10-01  -2.935886  2.02
667 2005-07-01  -1.307811  4.25
706 2008-10-01  11.563449  1.25

threefeather

x<-Ist[c(373,622,667,706),]
ir.pre<-predict(model.season_final,x,type = "response")
data.frame(Date=Ist$Date[c(373,622,667,706)],Prediction=ir.pre,Real=Ist$Interest.Rate[c(373,622,667,706)])
 
         Date Prediction  Real
373 1981-01-01   7.133682 13.00
622 2001-10-01  -1.161944  2.02
667 2005-07-01  -0.635116  4.25
706 2008-10-01   8.995944  1.25

threefeather

####Remove the large outlier and leverage points:373,445,622,667,706,709
Residuals:
    Min      1Q  Median      3Q     Max 
-4.2856 -0.8929  0.0166  0.8256  4.5421 

Coefficients:
                                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)                       17.078169   2.274732   7.508 5.09e-12 ***
unem.rate                         -1.165918   0.127226  -9.164 3.70e-16 ***
BankLoan                           1.354565   0.150751   8.985 1.06e-15 ***

threefeather

Call:
lm(formula = Interest.Rate ~ . - Date, data = Ist.complete_final)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.7459 -0.9721 -0.1388  0.9188  5.8663 

Coefficients:
                                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)                       13.09168    2.50591   5.224 5.56e-07 ***