This paper outlines a data mining approach to analysis and prediction of the trend of stock prices. The approach consists of three steps, namely parti- tioning, analysis and prediction. A modification of the commonly used k-means clustering algorithm is used to partition stock price time series data. After data partition, linear regression is used to analyse the trend within each cluster. The results of the linear regression are then used for trend prediction for windowed time series data. The approach is efficient and effective at predicting forward trends of stock prices. Using our trend prediction methodology, we propose a trading strategy TTP (Trading based on Trend Prediction). Some preliminary results of applying TTP to stock trading are reported.
Joint Conference on Information Sciences, Advances in Intelligent Systems Research