Modelling extremal events: for insurance and finance
Modelling extremal events: for insurance and finance
Statistical analysis of extreme values
Statistical analysis of extreme values
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
On-Line learning of decision trees in problems with unknown dynamics
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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We explore the role of sequences of extreme values for measuring tail-dependence between times series. The proposed measure concentrates on searching extreme cause-effect fluctuation pairs in the recent time interval and requires much less data than current causality and dependence approaches. The target applications of this approach are those in which there is the necessity of rapidly recognizing the interval time in which a time series may be influenced by other time series characterized by sudden and unpredictable extreme changes. This paper presents the tail-dependence measure in the field of stock markets and compares it to known causality and dependence measures. An application of the mentioned measure in the field of space physics is also presented.