A Structural Learning Algorithm Based on Covering Algorithm and Its Application in Stock Forecasting

Covering algorithms are very useful learning methods of neural networks, and their computing complexity is lower than that of the learning method based on search mechanism or programming based learning algorithm Covering algorithms not only are applied to deal with vast data set but also provide a new constructive learning method of neural networks However, they are based on the assumption that all of the training samples are accurate and the instance that some of the training samples are not accurate is not discussed If the methods are applied to no accurate data directly, the result is not satisfactory Discussed in this paper is the problem of forecasting time series where there are some no accurate data The covering algorithm improved and the definitions of covering intensity and no acknowledge sample are introduced The improved covering algorithm is called a structural learning algorithm (SLA) SLA is applied to forecasting a time series which is composed of Shanghai's stock integrating index, and the satisfying results are achieved It is expected that SLA will have wide applications