Learning Prediction of Time Series. A Theoretical
and Empirical Comparison of CBR with some
other Approaches

Gholamreza Nakhaeizadeh

Daimler-Benz AG, Research and Technology, F3W,

Wilhelm-Runge-Str. 11, Postfach 2360, D-89013 Ulm, Germany



Abstract
Case-based Reasoning (CBR) is a rather new research area in Artificial 
Intelligence. The concept of K-Nearest Neighbours (KNN) that can be
considered as a subarea of CBR traced back, however, to early fifties and
during the last years it is deeply investigated by the statistical community. 
In dealing with the task learning prediction of time series, besides
the KNN-approach, the Statistician have investigated other approaches
based on regression analysis and Box-Jenkins methods. Recently, neural
networks and symbolic machine learning approaches are applied to performing 
this task as well. Although learning prediction of time series is a
very important task in different scientific disciplines, there is no comprehensive 
study in the literature which compares the performance of CBR
with the performance of the other alternative approaches. The aim of this
paper is to contribute to this debate from a theoretical and empirical point
of view.
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