Fernando C. Colon Osorio
"While much work has been done in mining nominal sequential data much less has been done on mining numeric time series data. This stems primarily from the problems of relating numeric data, which likely contains error or other variations which make directly relating values difficult. To handle this problem, many algorithms first convert data into a sequence of events. In some cases these events are known a priori, but in others they are not. Our work evaluates a set of time series data instances in order to determine likely candidates for unknown underlying events. We use the concept of bounding envelopes to represent the area around a numeric time series in which the unknown noise-free points could exist. We then use an algorithm similar to Apriori to build up sets of envelope intersections. The areas created by these intersections represent common patterns found throughout the data."
Worcester Polytechnic Institute
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Stoecker-Sylvia, Zachary, "Mining for Frequent Events in Time Series" (2004). Masters Theses (All Theses, All Years). 1016.
envelopes, numeric, time series, events, mining, Data mining, Time-series analysis