Decision forest for multivariate time series analysis
Nine He, Leyang Li, Osamu Yoshie · 2011
Nowadays with time series accounting for an increasingly large fraction of world's supply of data, there has been an explosion of interest in mining time series data. This paper proposes a multivariate time series classification model which is both effective in classifier's accuracy and comprehensibility. It is composed of two stages: a supervised clustering for pattern extraction and soft discretization decision forest. In supervised clustering, some real time series instances from the training dataset will be selected as class dedicated patterns. While in decision forest, the rule induction helps to improve the knowledge acquisition of the classifier. In addition, soft discretization would further improve the accuracy and comprehensibility of the classifier.