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Granulation of financial time series for trend analysis and recognition

Time series in finance are characterized by upside and downside movements that can be or not part of a larger trend. Trends become more obvious when we look at data points as granules and we study the relationship between them. In this paper we discuss which role granulation can have in describing the behavior of time series. In particular we investigate granules obtained by means of Ruspini partitions and we provide some examples of application.