Abstract
We have many kinds of data of time series such as stock prices. We understand them via their natural language expressions. We, therefore, proposed a method to express a global trend of time series in a natural language. In this paper, we propose a method to extract outstanding local features to express largely varying time series. We generate data of time series for the global trend via fuzzy rule expressions of the global trend and extract local features as the position of large difference between the original data and the global trend. We apply the method to the data of Workshop on Multimodal Summarization for Trend Information (MuST).
