Hussain et al., (2019). pyMannKendall: a python package for non parametric Mann Kendall family of trend tests.. Journal of Open Source Software, 4(39), 1556, https://doi.org/10.21105/joss.01556
Mann Kendall trend test is suitable for analyzing time series data with continuous growth or downward trend (monotonic trend). It is a nonparametric test, which is applicable to all distributions (i.e. the data does not need to meet the assumption of normal distribution), but the data should have no sequence correlation. If the data have sequence correlation, it will have an impact on the significance level (P value). In order to solve this problem, the researchers proposed several improved Mann Kendall tests (Hamed and Rao improved MK test, Yue and Wang improved MK test, etc.). Seasonal Mann Kendall test can also exclude the influence of seasonality.
Installation: need python environment;
Input: time series data
Output: slope,intercept, Significance level;
Dependent library files: numpy，scipy
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