A STUDY ON SHORT-TERM PREDICTION OF ECONOMY DATA USING CHAOS ANALYSIS
The movement of financial time series such as the stock prices and currency exchange was analyzed by ap plying linear theory to the economic data. The portion of the change which deviated from that predicted by line ar theory was handled as an error term. However analysis using only linear theory is inadequate and caused by complex parameters. Recently, deterministic chaos theory has been applied to irregularly changing time series data in fields such as physics, biology, engineering and social science. Examples include weather, earth quakes, sunspots, and so on. It has been shown that such phenomena can be more accurately analyzed and forecasted by using the chaos theory 1 2. In this study we examines whether chaotic behavior exists in financial time series data such as stock prices by using Lyapunov exponents and fractal dimensions, and attempt to predict the financial index from chaos theory.
Chaos Lyapnov exponents Prediction Fractal dimensions.
Yutaka Fujihara Sinichi Ikeda Kenji Nakamura Fengzhi Dai
Department of Control Engneering, Matsue College of Technology, Japan Advanced Engineering Faculty, Matsue College of Technogy, Japan
国际会议
The Ninth International Conference on Industrial Management(第九届工业管理国际会议 ICIM2008)
日本大阪
英文
381-385
2008-09-16（万方平台首次上网日期，不代表论文的发表时间）