Recursive temporal meta-cluster of daily time series

Abstract

Identifying pattern groups from large temporal data sets, preserving clustering schemes obtained from different heuristic algorithms and presenting temporal pattern profiles for a specific day and previous days are significant concerns in many fields. As clustering schemes created by different heuristic algorithms may not completely agree with each other, researchers have proposed different clustering ensemble techniques to combine such schemes. In the first phase, this research proposes a rough set based ensemble method that preserves the inherent order in clustering. In the second phase, the Recursive Meta-cluster algorithm is used to create meta-profiles having current volatility with historical perspective for the financial daily temporal pattern clusters, which a trader may use while making decisions. Traditionally, any information of the historical or future clustering is not considered for temporal clustering. The proposed algorithm clusters the temporal patterns iteratively using previous clustering results from connected historical patterns.