Elastic distances for time-series classification: Itakura versus Sakoe-Chiba constraints

Knowledge and Information Systems - In the field of time series data mining, the accuracy of the simple, but very successful nearest neighbor (NN) classifier directly depends on the chosen...     

Case‐based curve behaviour prediction

Case‐based reasoning (CBR) is the area of artificial intelligence where problems are solved by adapting solutions that worked for similar problems from the past. This technique can be applied in different domains and with different problem representations. In this paper, a system curve base generator (CuBaGe) is presented. This framework is designed to be a domain‐independent prediction system for the analysis and prediction of curves and time‐series trends, based on the CBR technology. CuBaGe employs a novel curve representation method based on splines and a corresponding similarity function based on definite integrals. This combination of curve representation and similarity measure showed excellent results with sparse and non‐equidistant time series, which is demonstrated through a set of experiments. Copyright © 2008 John Wiley & Sons, Ltd.