Mann-Kendall Trend Test and Sen's Slope Estimation with MATLAB Implementation

This MATLAB implementation provides complete source code for performing Mann-Kendall trend testing and Sen's slope estimation, which I'm pleased to share with the community. The Mann-Kendall trend test is a widely used non-parametric statistical method for detecting monotonic trends in time series data. This approach is particularly valuable for analyzing diverse datasets including environmental, economic, and social science data. The implementation handles trend detection through rank-based comparisons of all data pairs, making it robust against outliers and non-normal distributions. Sen's slope estimation method calculates the median slope between all possible pairs of data points, offering superior robustness compared to ordinary least squares regression by using median values instead of means. Our code implementation efficiently computes pairwise slopes and determines the median slope estimate with proper handling of tied values. Beyond the core statistical functions, this source code includes comprehensive data visualization features that generate trend analysis plots, seasonal decomposition charts, and statistical summary graphics. The visualization module helps users intuitively understand their data patterns and interpret the statistical results effectively. We hope this implementation assists researchers in conducting robust trend analysis on their datasets. The code is structured with clear function documentation and includes example usage scenarios. For any technical questions or enhancement suggestions, please feel free to contact us through the repository's issue tracking system.