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We investigate the effectiveness of neural networks for
predicting the future behavior of seasonal time series.
Utilizing the training set constructed properly, we can train
the network who can be used to predict the future of seasonal
time series. A shifting-learning method is also employed in
order to obtained a better forecasting performance. The
quarterly imports of goods and services of Taiwan between the
first quarter of 1968 and the fourth quarter of 1990 are
studied in the research. The series are contaminated with
outliers, which will increase the difficulty of forecasting.
Empirical results exhibit that neural networks model free
approach have better prediction performance than the classical
Box-Jenkins approach, even the series are contaminated with
outliers.
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