Domains in which time plays a relevant role, CBR systems require temporal similarity measures to compare cases. Temporal cases are traditionally represented by a set of temporal features, describing time series and temporal sequences. In the particular situation where these features are not homogeneous (i.e. combination of qualitative and quantitative information such as the patient evolution i...

Coping with time series cases is becoming an important issue in employing case based reasoning (CBR) in many industrial and medical applications. This paper studies the representation and similarity matching of symbolic time series data that are generated from an uncertain (nondeterministic) process over time. We propose a method to convert such (lengthy) time series into a concise form to capt...

Respiratory Sinus Arrhythmia has until now been analysed manually by reviewing long time series of heart rate measurements. Patterns are identified in the analysis of the measurements. We propose a design for a classification system of Respiratory Sinus Arrhythmia by time series analysis of heart and respiration measurements. The classification uses Case-Based Reasoning and Rule-Based Reasoning...

Time series are ubiquitous, and a measure to assess their similarity is a core part of many systems, including case-based reasoning systems. Although several proposals have been made, still the more robust and reliable time series similarity measures are the classical ones, introduced long time ago. In this paper we propose a new approach to time series similarity based on the costs of iterativ...

The temporal dimension of the knowledge embedded in cases has often been neglected or oversimplified in Case Based Reasoning systems. However, in several real world problems a case should capture the evolution of the observed phenomenon over time. To this end, we propose to represent temporal information at two levels: (1) at the case level, if some features describe parameters varying within a...

Zooplankton are considered good indicators for understanding how oceans are affected by climate change. While climate influence on zooplankton abundance variability is currently accepted, its mechanisms are not understood, and prediction is not yet possible. This paper utilizes the Genetic Programming approach to identify which environmental variables, and at which extent, can be used to expres...