  The paper is focused on the forecasting method for time series groups with
the use of algorithms for cluster analysis. $K$-means algorithm is suggested to
be a basic one for clustering. The coordinates of the centers of clusters have
been put in correspondence with summarizing time series data the centroids of
the clusters. A description of time series, the centroids of the clusters, is
implemented with the use of forecasting models. They are based on strict binary
trees and a modified clonal selection algorithm. With the help of such
forecasting models, the possibility of forming analytic dependences is shown.
It is suggested to use a common forecasting model, which is constructed for
time series the centroid of the cluster, in forecasting the private
(individual) time series in the cluster. The promising application of the
suggested method for grouped time series forecasting is demonstrated.
