Is ARIMA better in comparision with Neural Networks?

Question

After working on Backpropagation Neural Network and ARIMA Time Series Model, I asked myself which one is better, but can't figure out the answer. They both use different approaches on the same problem (future prediction). Please can someone help me stating the obvious.

Backpropagation(C++):

 typedef struct {                     /* A LAYER OF A NET:                     */
    INT           Units;         /* - number of units in this layer       */
    REAL*         Output;        /* - output of ith unit                  */
    REAL*         Error;         /* - error term of ith unit              */
    REAL**        Weight;        /* - connection weights to ith unit      */
    REAL**        WeightSave;    /* - saved weights for stopped training  */
    REAL**        dWeight;       /* - last weight deltas for momentum     */
} LAYER;

typedef struct {                     /* A NET:                                */
    LAYER**       Layer;         /* - layers of this net                  */
    LAYER*        InputLayer;    /* - input layer                         */
    LAYER*        OutputLayer;   /* - output layer                        */
    REAL          Alpha;         /* - momentum factor                     */
    REAL          Eta;           /* - learning rate                       */
    REAL          Gain;          /* - gain of sigmoid function            */
    REAL          Error;         /* - total net error                     */
} NET;

ARIMA(R):

arima(stockadj,order=c(best.model[1],best.model[2],best.model[3]),xreg=1:n)
stockfor<-predict(stockari,h=100,newxreg=(n+1):(n+100))
ts.plot(stockadj,stockfor$pred,ylab="Original+Predicted Values",main="Forecast")   

Answer 1

It looks like you are using both of these models for time-series forecasts. I would cross-validate both models and compare their out-of-sample error.

Answer 2

NN ignore outliers. If you ignore outliers, then you are in big trouble.

Your ARIMA model is also ignoring outliers so then you are also in big trouble.

As for cross-validating, that is for those that are fitting models to data instead of actually modeling. Only the 849 page text book "Principles of Forecasting" agrees with me on this statement, BUT if you have taken care of all the things you need to then you can be so dumb. See the reference here.

4.6 Obtain the most recent data

See more on outliers here.