Abstract
Decision trees are one of the most popular methods for acquiring knowledge from data. In this paper, we try to acquire knowledge from data of time series using a global trend and local features and oscillation of time series in
addition to the current value in each attribute. This method generate rules such as "if a global trend is slightly increasing and there is a big wave in the last stage, then this data is classified into C1". We use a method based on Fuzzy ID3 algorithm and the expression of the data of time series in a natural language in the previous research. We apply the method to the data of stock prices, where we acquire rules from the data of attributes, global trend, local features, oscillation and category of business, and the class.
