Man-Kendall Trend Analysis on Time Series

This process performs a Man-Kendall trend analysis to identify the presence or absence of significant trends in time series data. It returns statistical analysis coefficients (e.g., p-values, tau, and slope) for each defined group, allowing for group-wise trend identification (R function 'trend_analysis_mk').

daugava use case AquaINFRA trend_analysis_mk R

Inputs
Id	Title	Data Type	Description
input_data	Input table	string	URL to the input table containing the time series data. This table includes grouping identifiers (if applicable), columns for time (e.g., year, month) and values to be analyzed for trends. For example, use the result from ts_selection_interpolation.
colnames_relevant	Column names identifying group(s)	string	Column name(s) identifying relevant groups in the dataset. These columns will be used to define unique groups for which separate trend analyses are performed.
colname_time	Column name for time	string	The name of the column containing the time variable (e.g., year, month) to be used in the trend analysis. Example = "year".
colname_value	Column name for values	string	The name of the column containing the values to be analyzed in the Man-Kendall trend test.

Outputs
Id	Title	Description
trend_analysis_results	Man-Kendall trend analysis	A table containing the statistical results of the Man-Kendall trend analysis for each unique group defined by colnames_relevant. The table includes coefficients such as tau, p-values, and slope for each group's time series.

Man-Kendall Trend Analysis on Time Series

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