#StackBounty: #time-series #trend #discrete-data Checking assumptions for Mann-Kendall trend test

Bounty: 50

I have a data on counts of images submitted with requests overtime. I would like to test the null hypothesis of no trend vs alternative hypothesis that there is a downward trend.

My data is a sequence of numbers [2,3,5,6,2,4,5…], where every number represents number of images submitted with request. Order is chronological.

There are two things I am concerned about.

First how can I check for no serial dependence? Data is discrete not continuous – I ma not to sure how to test for serial dependence in this case. Is simple ACF/PACF plot good test?

Second I have around 200 observations but the range of possible values is small, X is form 1 to 8, hence I have a lot of tied groups. Is this a problem for Mann-Kendall trend test?


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