Predicting Rare Extreme Values

Modelling extreme data is very important in several application domains, like for instance finance, meteorology, ecology, etc.. This paper addresses the problem of predicting extreme values of a continuous variable. The main distinguishing feature of our target applications resides on the fact that these values are rare. Any prediction model is obtained by some sort of search process guided by a pre-specified evaluation criterion. In this work we argue against the use of standard criteria for evaluating regression models in the context of our target applications. We propose a new predictive performance metric for this class of problems that our experiments show to perform better in distinguishing models that are more accurate at rare extreme values. This new evaluation metric could be used as the basis for developing better models in terms of rare extreme values prediction.

Authors and Affiliations

1. LIACC-FEP, University of Porto, R. de Ceuta, 118, 6., Porto, 4050-190, Portugal

   Luis Torgo & Rita Ribeiro

Editors and Affiliations

1. Nanyang Technological University, Singapore

   Wee-Keong Ng

2. Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, 153-8505, Tokyo, Japan

   Masaru Kitsuregawa

3. School of Computer Science and Technology, Heilongjiang University, China

   Jianzhong Li

4. School of Computer Engineering, Nanyang Technological University, 639798, Singapore, Singapore

   Kuiyu Chang

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