I have built an ARIMAX model in python for predicting a time series. After presenting my findings ive been asked what robustness tests i have used. My skillset is more on the python side. I only have basic statistics training and i dont know how to answer this question. I can obviously review the model output (coefficients, p-values, errors) but my understanding is that robustness refers to:
statistics that yield good performance when data is drawn from a wide range of probability distributions that are largely unaffected by outliers or small departures from model assumptions in a given dataset. (https://www.thoughtco.com/what-is-robustness-in-statistics-3126323)
How do i do this?
- If i run the arimax using various different train/test subsets of my time series, would that qualify as a robustness test? How many times should i do this?
- Are there any standardised robustness tests for an arimax prediction? What are they?
