A fuzzy time series data representation method based on the Japanese candlestick theory is proposed and used in assisting financial prediction. The Japanese candlestick theory is an empirical model of investment decision. The theory assumes that the candlestick patterns reflect the psychology of the market, and the investors can make their investment decision based on the identified candlestick patterns. We model the imprecise and vague candlestick patterns with fuzzy linguistic variables and transfer the financial time series data to fuzzy candlestick patterns for pattern recognition. A fuzzy candlestick pattern can bridge the gap between the investors and the system designer because it is visual, computable, and modifiable. The investors are not only able to understand the prediction process, but also to improve the efficiency of prediction results. The proposed approach is applied to financial time series forecasting problem for demonstration. By the prototype system which has been established, the investment expertise can be stored in the knowledge base, and the fuzzy candlestick pattern can also be identified automatically from a large amount of the financial trading data.

An intelligent tool for the investment knowledge learning and sharing is proposed and developed. The candlestick pattern is a widely used empirical model of investment decision and the investors can make investment decisions by the identified candlestick patterns. The imprecise and vague investment knowledge is represented in fuzzy candlestick patterns. The pattern consists of candlestick lines which are derived from the variation of the trading prices. The investor can create, edit, validate, and share the patterns by the proposed system. A graphical user interface (GUI) is designed to display the candlestick patterns and the user can also capture the pattern directly from the GUI. A fuzzy pattern recognition module is implemented in the system to help the user to identify the patterns automatically from the large amount financial database.