Eamonn Keogh says 100-year-old statistical process control beats state of the art on TSB-AD-M.

Keogh posted to r/MachineLearning (369 upvotes) that on most of the benchmark datasets used across NeurIPS, SIGKDD and VLDB time-series anomaly detection papers, plain SPC matches or beats the published SOTA, scoring perfectly on the ECG trace he shows and trivially on the TAO traces. His argument isn't that the algorithms are wrong, it's that the benchmark is too easy to support the claims built on it, and he notes one dataset is a classification problem solved 27 years ago converted to a TSAD task without introspection. (r/MachineLearning) He says he's done 90% of the work on harder replacements using sled dogs, tuna, fuel cells and smart manufacturing.