Expression of Time Series in Natural Language with Global Trends and Local Features ---Tuning of Fuzzy Intervals in the Temporal Axis

Abstract

We have many kinds of data of time series such as stock prices. We have proposed a method to express a global trend of them in a natural language via representative values on the fuzzy intervals in the temporal axis and local features via the position of large difference between the original data and the data generated from the global trends. In the previous research we used the fixed fuzzy intervals for various data of time series. However, humans usually tune them with local features for every data of time series. In this paper, we propose a method to tune the fuzzy intervals in the temporal axis by reducing difference between the original data and the data generated from the global trend. As a result, the fuzzy intervals have been tuned by local features, with the global trend being expressed in more appropriate word.