Stock market co-movement assessment using a three-phase clustering method
Expert Systems with Applications: An International Journal
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An effective similarity measure approach on specific data sets is becoming the focus in time series data mining. To solve the problem that financial time series are lacking dynamic information of trend after they are deal with dimension reduction with SAX, in this work we propose a novel similarity measure function, Composite-Distance-Function which joins point-distance advantages and trend-distance advantages together. Through the experiments of SAX with different distance function, we prove that Composite-Distance-Function is a useful function which provides new ideas to reveal the interdependence between the financial data and helps to solve the problem of time series similarity.