Kolmogorov complexity spectrum for use in analysis of UV-B radiation time series

TL;DR

This study applies Kolmogorov complexity and sample entropy measures to analyze UV-B radiation time series in Serbia, revealing a decrease in complexity from 1990-1998 to 1999-2007, likely due to human activity and climate change.

Contribution

It introduces the Kolmogorov complexity spectrum and the KLM metric for analyzing environmental time series data.

Findings

Complexity decreased in the later period, indicating environmental changes.

The methods effectively differentiate between different time intervals.

Data from multiple sources were consistently analyzed using the proposed measures.

Abstract

We have used the Kolmogorov complexity and sample entropy measures to estimate the complexity of the UV-B radiation time series in the Vojvodina region (Serbia) for the period 1990-2007. We defined the Kolmogorov complexity spectrum and have introduced the Kolmogorov complexity spectrum highest value (KLM). We have established the UV-B radiation time series on the basis of their daily sum (dose) for seven representative places in this region using (i) measured data, (ii) data calculated via a derived empirical formula and (iii) data obtained by a parametric UV radiation model. We have calculated the Kolmogorov complexity (KL) based on the Lempel-Ziv Algorithm (LZA), KLM and Sample Entropy (SE) values for each time series. We have divided the period 1990-2007 into two sub-intervals: (a) 1990-1998 and (b)1999-2007 and calculated the KL, KLM and SE values for the various time series in these sub-intervals. It is found that during the period 1999-2007, there is a decrease in the KL, KLM and SE, comparing to the period 1990-1998. This complexity loss may be attributed to (i) the increased human intervention in the post civil war period causing increase of the air pollution and (ii) the increased cloudiness due to climate changes.