trend-analysis

To detect statistically significant trends in time series data, use linear regression for normal data and the Mann-Kendall test for non-normal distributions. This identifies long-term directional shifts while accounting for outliers in environmental or financial records.

Use the Mann-Kendall test for trend analysis when your time series data lacks normality or contains outliers. Linear regression is parametric and best for normal data, whereas the non-parametric Mann-Kendall test provides robust trend detection without those distribution assumptions.

You calculate Sen's slope and the Mann-Kendall p-value for precipitation data by applying the pymannkendall library to your CSV dataset. This outputs a robust slope estimate and significance p-value to confirm if long-term climate patterns are statistically shifting.

Yes, trend analysis works with pandas dataframes for environmental data. It relies on pandas for data handling alongside scipy for regression and pymannkendall for significance testing, enabling you to process annual temperature or precipitation records efficiently.

The best way to analyze climate pattern shifts in annual temperature records is applying both parametric and non-parametric trend tests. Combining linear regression with the Mann-Kendall test determines whether a region experiences a statistically significant long-term increase or decrease.

A limitation of linear regression for time series trend analysis is its sensitivity to outliers and requirement for normally distributed data. When these assumptions fail, the non-parametric Mann-Kendall test and Sen's slope provide a more robust alternative for identifying long-term trends.