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Time series similarity evaluation is a crucial processing task performed either as a stand-alone action or as a part of extensive data analysis schemes. Among essential procedures that rely on measuring time series similarity, we find time series clustering and classification. While the similarity of regular (not temporal) data frames is studied extensively, there are not many methods that account for the time flow. In particular, there is a need for methods that are easy to interpret by a human being. In this paper, we present a concept-based approach for time series similarity evaluation. Firstly, a global model describing a given dataset of time series (made of two or more time series) is built. Then, for each time series in the dataset, we create the corresponding local model. Comparing time series is performed with the aid of their local models instead of raw time series values. In the paper, the described processing scheme is implemented using fuzzy sets representing concepts. The proposed approach has been applied to the task of time series classification, yielding highly satisfactory results. |
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