Consensus Classification for A Set of Multiple Time Series

Abstract

In multiple time series analysis, when there are a very large number of series, a classification into homogeneous clusters might be useful to reduce the problem’s complexity and eliminate possible redundancies (Zani, 1983). Furthermore, when we have different classifications, one for each statistical unit (e. g. spatial units), a consensus classification allows one to obtain a classification which summarizes the given classifications. The present paper focuses on the problem of identifying consensus classifications in a set of multiple time series (panel data), using a consensus method (Vichi, 1993, 1994). First, a distance among time series is defined and a hierarchical classification among time series, for each temporal lag and for each unit, is performed. Then, a consensus classification among different units for the same temporal lag is carried out. Finally, a hierarchical classification among the different consensus classifications, with the same temporal lag, is carried out.