Forecasting Chaotic Stock Market Data using Time Series Data Mining

An important financial subject that has attracted researchers' attention for many years is forecasting stock return. Many researchers have contributed in this area of chaotic forecast in their ways. Among them data mining techniques have been successfully shown to generate high forecasting accuracy of stock price movement. Nowadays, instead of a single aspects of stock market, traders need to use various aspects' forecasting to gain multiple signals and more information about the future of the markets. Aspects of Lyapunov, Entropy and Variance (ALEV) provide an approach for mining large stocks of time series data. This paper proposes a novel method for forecasting chaotic behavior of stock market's opening, high, low and closing price with time series data mining. The outcome of this study tries to help the investors in the stock market to decide the better timing for buying or selling stocks based on the knowledge extracted from the historical prices of such stocks.