mann-kendall trend analysis
  • Category: Deterministic Change Analysis
  • Development language: Python
  • Operating platform / Operating system: Linux\Windows
  • Compilation tools and environment: Python3
  • GitHub download path: https://github.com/Three-Poles/Time-series-analysis
  • Citation:

    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

  • Method description:
  • 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




    • 安装方式:安装python

      运行方式:在python下运行

      输入变量:时间序列数据

      输出变量:斜率、截距、显著性水平

      依赖库文件:numpy,scipy

      二维码: