Retrieval of similar time series with similarity degree of linguistic expressions for global trend and local features

We have various kinds of time series such as stock prices. We understand them via their linguistic expressions in a natural language rather than conventional stochastic models. We have proposed a method to extract their linguistic expressions for global trend and local features in a natural language. In this paper we propose a similarity degree of linguistic expressions for retrieving similar time series. And we tune the terms of the temporal axis to have better linguistic expressions. Then we illustrate retrieval of similar time series by our similarity degree.