Forecasting Method for Grouped Time Series with the Use of k-Means Algorithm

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 cor- respondence 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 possibil- ity 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