Mann-Kendall Trend Test MATLAB Implementation: Code and Algorithm Explanation

In this article, we explore how to perform trend analysis using the Mann-Kendall trend test in MATLAB. The Mann-Kendall trend test is a non-parametric statistical method used to detect trends in time series data. This approach is widely applied across various domains including meteorology, environmental science, economics, and many others. The MATLAB implementation typically involves calculating the S statistic by comparing all possible pairs of data points in the time series. The algorithm counts the number of increasing pairs minus the number of decreasing pairs, then normalizes this value to compute the standardized test statistic Z. Key functions involved include handling missing data, calculating variance corrections for tied values, and determining statistical significance through p-value calculation. We will discuss the fundamental concepts and principles behind the Mann-Kendall test and demonstrate its practical application in MATLAB for trend analysis. The implementation includes code segments for data preprocessing, trend calculation, and result interpretation. We'll examine how to properly interpret the test results, including understanding the direction and significance of detected trends, and how to apply this method in real-world scenarios. For those interested in trend analysis, this article provides valuable information and guidance to better understand and implement the Mann-Kendall trend test, featuring practical MATLAB code examples and statistical interpretation techniques suitable for research and professional applications.