Characterization of time series for analyzing of the evolution of time series clusters

• We propose a characterization of time series for multivariable temporal databases.
• For the characterization of time-series we used the level and trend components.
• The characterization the time-series is adequate for long and short periods of time.
• Our proposal allows analysis the evolution of the groups and objects.
• We developed an R-based script for generating multivariable temporal data base.

This work proposes a new approach for the characterization of time series in databases (temporal databases – TDB) for temporal analysis of clusters. For the characterization of time-series it were used the level and trend components calculated through the Holt-Winters smoothing model. For the temporal analysis of those clusters, it was used in a combined manner the AGNES (Agglomerative Hierarchical Cluster) and PAM (Partition Clustering) techniques. For the application of this methodology an R-based script for generating synthetic TDBs was developed. Our proposal allows the evaluation of the clusters, both in the object movement such as in the appearance or disappearance of clusters. The model chosen to characterize the time-series is adequate because it can be applied for short periods of time in situations where changes should be promptly detected for quick decision making.