Prediction Model Of Elliott Wave Theory Based On CBR

Securities investment market has became an important part of modern economic activities, it is attracting more and more investors to participate in them. The securities market is a high risk and high return investment field, it has important significance and application value of technical analysis. The stock time series contains a large number of potential knowledge and the laws of development of things, The Elliott wave theory was found based on large amount of observation, The Elliott wave theory wave theory and the nature are similar that have inherent laws,and there are a lot of similar structures in mathematics.The Elliott wave theory is produced in the nineteen thirties of the United States, it is the perfect way to describe the operation of the stock market. The Elliott wave theory has become an important means of the securities market, the number of waves of Eliot wave is more complex, with the naked eye is prone to misjudgment. less sentenced phenomenon. Different people using the Elliott wave theory may have a completely different result. This paper uses the computer to identify the stock in the Elliott wave model, and to comply with certain mathematical relationship.The stock market in the long-standing lag effect, this paper proves that the Elliott waves are also lagging effect. Operation of the system, the typical cycling model Elliott waves as examples, in the securities market random collection of a large number of Elliott wave typical cycling model. The time periods appear typical cycling model Eliot waves, with K-means cluster analysis. The results indicate the presence of typical cycling model Eliot waves lag effect in Chinese A share market.As the stock market is lagging effect exists, and there is a certain relationship. In this paper, using case-based reasoning (CBR) to predict the trend of the stock market. Case-based reasoning technique is an important method of artificial intelligence, the basic idea is to reuse experience. Case elements include volume, price, the relative strength index and the moving average index. In order to quickly find answers to new questions, index table using k-means clustering analysis results of establishing case search, meet new cases will be preferred in these stocks in search of answers to questions. The case base and the index table is stored in the SQL Sever2005, the structure of the whole system is the MATLAB+ODBC+SQL Sever. The new case and the old case matching algorithm using NN algorithm,if the similarity is higher, the higher the accuracy rate of prediction results. From the experimental results, the predicted effect is accurate, the operation of the system is reliable.