The idea is easy to grasp: create an extensive library of time series data and time series analysis methods, run all of the methods on each series, and then explore the relationships between them by analysing the output.
Clearly a marathon effort but one that pays big dividends. The field of time series analysis is much too broad and interdisciplinary for anyone to be across it all. How do we then find the right method to use for a given problem? Or how do we assess the value or novelty of any new proposed method? Such questions are now easy to tackle at scale.
Want to analyse a new dataset? Run all of the methods on your data to find which ones work well, which ones are effectively equivalent (highly correlated output) and which ones are complementary (uncorrelated output).
Want to assess your new method? Run it on all of the data and see how similar it is to existing methods. You might discover that someone has already created something similar in a completely different field!
I can see this being a very valuable exploratory tool for time series data analysis. It could be a convenient and effective replacement for a literature review. Why look up lots of papers and try to judge what might work, when you can just run everything and then let your judgement be guided empirically?
Ben made the point that many time series papers do very little actual comparison. I guess part of the explanation is the fact that the field is so broad that it feels almost futile. Now we have a way of doing this systematically.
To their credit, Ben and his colleagues have made their library and tools available online for the community to use. I look forward to trying it out.