Discovery Association Rules in Time Series Data
Abstract
Rule discovery from time series data is a data mining technique that tries to find relationships of sequential data. Finding association rules from time series data is different from finding such rules in traditional data because time series data is orderly data with a sequence that must be preserved. Many researchers have proposed many methods of analyzing and mining time series data, but most of them did not focus on finding association rules, and the data used in their experimentations were discretized symbols. In fact, many situations collect data in continuous numeration time series. In this paper, we propose a novel technique to find association rules from time series data. Our technique can analyze either the numerical time series or the symbolic time t series and show the resulting rules as X ⎯⎯→Y, which means that the group of pattern Y should
Authors: Kittipong Warasup, Chakarida Nukoolkit