Incremental prediction for sequential data

Abstract

Ensemble methods of incremental prediction for sequences refer to learning from new reference data that become available after the model has already been created from a previously available data set. The main obstacle in the prediction of sequential values in the real environment with huge amount of data is the integration of knowledge stored in the previously obtained models and the new knowledge derived from the incrementally acquired new increases of the data. In the paper, the new approach of the ensemble incremental learning for prediction of sequences was proposed as well as examined using real debt recovery data. © 2010 Springer-Verlag Berlin Heidelberg.