Linguistic Expression of Time Series and Its Applications to Retrieval and Machine Learning

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We have many kinds of data of time series such as daily temperatures and stock prices. It is likely that we understand them via linguistic expressions for the global trend and local features. In this research, we formulate a method to extract linguistic expressions for the global trend, local features and oscillation using fuzzy sets and implement a system for extracting them from a time series data. Then we implement two prototype systems for retrieving similar time series in a database of time series and learning knowledge from a set of data including time series. The retrieval of similar time series is based on a similarity degrees of linguistic expressions for global trend, local features and oscillation. The learning of knowledge is an extension of fuzzy C4.5 to including attributes of global trend, local features and oscillation.