Time Series Forecasting with Neural Networks : A Case
نویسنده
چکیده
SUMMARY Using the famous airline data as the main example, a variety of neural network (NN) models are tted and the resulting forecasts are compared with those obtained from the (Box-Jenkins) seasonal ARIMA model called the airline model. The results suggest that there is plenty of scope for going badly wrong with NN models and that it is unwise to apply them blindly in`black-box' mode. Rather the wise analyst needs to use traditional modelling skills to select a good NN model, for example in making a careful choice of input variables. The BIC criterion is recommended for comparing diierent NN models. Great care is also needed when tting a NN model and using it to produce forecasts. Methods of examining the response surface implied by a NN model are examined as well as alternative procedures using Generalized Additive Models and Projection Pursuit Regression.
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