Abstract
Recently, recurrence plot (RP) and its quantification techniques have become an important research tool in nonlinear analysis. In the existing researches, an RP is directly established on a time series ignoring the influence of noise on data, which will affect our judgement on the dynamic properties of a system. To tackle the problem there, this paper proposes a novel recurrence plot, namely fuzzy granular recurrence plot (FGRP). An FGRP of a time series is built not directly on the time series itself but on its corresponding granular time series which is composed of fuzzy information granules. With specific capability, fuzzy information granules are used as building blocks of an FGRP to achieve high-level, compact and understandable signal models. In order to apply the FGRP method to time series classification tasks, an FGRP based classification model is designed in this paper. Subsequent experiments show that the FGRP of a time series can reduce the effect of noise, and the FGRP based classification model can improve the classification performance.
Published Version
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