You can beat SOTA Time Series Anomaly Detection methods with a 100 year old algorithm [R]

You can beat SOTA Time Series Anomaly Detection methods with a 100 year old algorithm

Time Series Anomaly Detection (TSAD) seems to be one of the hottest topics in NeurIPS, SIGKDD, VLDB etc.

Many (perhaps most) papers evaluate on Paparrizos’ TSB-AD-M benchmark…

However, I tested these benchmark datasets and found that in most cases I could beat the SOTA TSAD methods with a 100-year-old algorithm, simple Statistical Process Control (SPC). In the attached example, SPC gets perfect results.

If we can beat the SOTA papers with 100-year-old algorithm, we probably should not be too impressed with them [b]. I really think this calls for some introspection by the community.

To be clear, I make no claims (here) about the proposed algorithms in all these paper. But the TSB-AD benchmark is obviously too trivial to make meaningful claims on [a][b].

The example shown is one of the ECG traces but look at dozen of traces marked “TAO”, they are even more trivial to solve with SPC [a][c].

I do not claim to have solved the triviality problem, but I have done 90% of the work to introduce more challenging TSAD problems ([d] sled dogs, [e] Tuna, Fuel Cells, Smart Manufacturing etc.).

TLDR: I think the TSAD community needs more introspection on benchmarks. Most progress over the last decade seems to be illusionary.

[a] https://www.youtube.com/watch?v=VftCMSI3C_s

[b] https://www.dropbox.com/scl/fi/31zuyhejb6sdjrom20frn/Problems-with-Time-Series-Anomaly-Detection.pptx?rlkey=mvcj1wz5s45kgazezopnih2h7&dl=0

[c] https://www.dropbox.com/scl/fi/42fkf9q9hft2224dnm83v/The-TSB-AD-Benchmarks-are-Nonsense.pptx?rlkey=5fwjopie5ncjhkgr0wqhdm2lp&dl=0

[d] https://www.linkedin.com/feed/update/urn:li:activity:7488825356494237696/

[e] https://www.dropbox.com/scl/fi/hettphvtpyrksggfect9d/Tutorial-on-Pan-Matrix-Profile.pptx?rlkey=p59gd2w56fxl9kl2fh5q819oo&dl=0