Abstract
This article examines several data mining approaches that perform short time series analysis. The basis of the methods is formed by clustering algorithms with or without modifications. The proposed methods implement short time series analysis when the numbers of the observations are not equal and the historical information is short. The inspected approaches are offered for solving complex tasks where statistical analysis methods cannot be applied or their functioning does not provide the necessary efficiency. The proposed methods are based on grid-based clustering and k-means algorithm modifications.
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