An economic phenomenon predicting and/or analyzing system using a neural network. In the disclosed system, time series data indicating economic phenomena are input to preparation modules, and moving-average values and their differences are generated. One of the preparation modules performs a predetermined process over the time series data indicating an economic phenomenon, i.e. the change of TOPIX, to remove trends. A pattern sorter sorts the trend-free data into a certain number of groups. Average values of various time series data, their differences and the result of pattern sorting are input to input layer neurons of the network. The network is provided in advance with learning information of the change of TOPIX in the past. The output of the output layer neurons will be a value of prediction of the change of TOPIX. For the output of hidden layer neurons, principal components are obtained by principal analysis modules. A correlation analysis module obtains a distribution of frequency of principal component rankings and analyzes the correlation between the explanation variants and the output of the neural network based on the obtained distribution of frequency.
Economic phenomenon predicting and analyzing system using neural network