We investigate ways to use knowledge and network learning techniques to improve neural multivariate prediction ability. The prediction of daily stock prices was taken as an example of a complicated real-world problem. We make use of prior knowledge of stock price predictions and newspaper information on domestic and foreign events. Event-knowledge is extracted from newspaper headlines according to prior knowledge. We choose several economic indicators according to prior knowledge and input them together with event-knowledge into neural networks. Also used is a selective presentation learning technique for improving the ability to predict large changes by neural networks. We present training data that correspond to large changes in the prediction-target time series more often than those corresponding to small changes. The effectiveness of our approach is shown experimentally.
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