Visualizing the trend of the data
In this, we will be plotting the data, Visualizing its trend, and matching the trend.
Python3
import matplotlib.pyplot as pltgfg_data = [54, 52, 53, 59, 56, 57, 51, 52, 50, 53]plt.plot(gfg_data) |
Output:
Example 2: Mann-Kendall Trend test on the trend present in the data:
Python3
import pymannkendall as mkgfg_data = [1, 2, 3, 4, 5]# perform Mann-Kendall Trend Testmk.original_test(gfg_data) |
Output:
Mann_Kendall_Test(trend=’increasing’, h=True, p=0.0274863361115103, z=2.2045407685048604, Tau=1.0, s=10.0,
var_s=16.666666666666668, slope=1.0, intercept=1.0)
Output Interpretation:
Since in the above example, the p-value is 0.027 which is less than the threshold(0.5) which is the alpha(0.5) then we fail not to reject the accepted hypothesis i.e. we do have sufficient evidence to say that sample has a trend present.
How to Perform a Mann-Kendall Trend Test in Python
In this article, we will be looking at the various approaches to perform a Mann-Kendall test in Python.
Mann-Kendall Trend Test is used to determine whether or not a trend exists in time series data. It is a non-parametric test, meaning there is no underlying assumption made about the normality of the data.
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