Trend discovery in financial time series data using a case based fuzzy decision tree

In recent years, many attempts have been applied to predict the behavior of stock price’s movement. However, these attempts could not build an accurate and efficient stock trading system owing to the high dimensionality and non-stationary variations of stock price within a large historic database. To solve this problem, this paper applies fuzzy logic as a data mining process to generate decision trees from a stock database containing historical information. There are many attributes in the stock database and often it is impossible to develop a mathematical model to classify the data. This paper establishes a novel case based fuzzy decision tree model to identify the most important predicting attributes, and extract a set of fuzzy decision rules that can be used to predict the time series behavior in the future. The fuzzy decision tree generated from the stock database is then converted to fuzzy rules that can be further applied in decision-making of stock price’s movement based on its current condition. To demonstrate the effectiveness of the CBFDT model, it is experimentally compared with other approaches on Standard & Poor’s 500 (S&P500) index and some stocks in S&P500. The overall performances of CBFDT model are very convincing thus it provides a new implication of research in dealing with financial time series data.