Processing Large Outliers in Arrays of Observations

Abstract

The interest in large or extreme outliers in arrays of empirical information is caused by the wishes of users (with whom the author worked): specialists in medical and zoo geography, mining, the application of meteorology in fishing tasks, etc. The following motives are important for these specialists: the substantial significance of large emissions, the fear of errors in the study of large emissions by standard and previously used methods, the speed of information processing and the ease of interpretation of the results obtained. To meet these requirements, interval pattern recognition algorithms and the accompanying auxiliary computational procedures have been developed. These algorithms were designed for specific samples provided by the users (short samples, the presence of rare events in them or difficulties in the construction of interpretation scenarios). They have the common property that the original optimization procedures are built for them or well-known optimization procedures are used. This paper presents a series of results on processing observations by allocating large outliers as in a time series in planar and spatial observations. The algorithms presented in this paper differ in speed and sufficient validity in terms of the specially selected indicators. The proposed algorithms were previously tested on specific measurements and were accompanied by meaningful interpretations. According to the author, this paper is more applied than theoretical. However, to work with the proposed material, it is required to use a more diverse mathematical tool kit than the one that is traditionally used in the listed applications.

Preface to the Special Issue on Probability and Stochastic Processes with Applications to Communications, Systems and Networks

“…For the following purposes, specialists are important: the essential importance of large emissions, the fear of errors in the study of large emissions by standard and previously applied methods, the speed of information processing, and the ease of interpretation of the results obtained. To meet these requirements, algorithms for interval pattern recognition and accompanying auxiliary computational procedures were developed in [2]. These algorithms were developed for specific samples provided by users (short samples, the presence of rare events in them, or the difficulty of constructing interpretation scenarios).…”

“…The authors present a series of results on the processing of observations through the extraction of large outliers, both in the time series and in planar and spatial observations. The algorithms presented in [2] are fast and sufficiently valid in terms of specially selected indices and have been tested on specific measurements and accompanied by meaningful interpretations.…”