[eamonnkeogh]

Thanks for your kind words (I am one of the authors) For those interested, there is an expanded review/critique of datasets here [a]

There is also a video here [b]

[a] https://www.dropbox.com/scl/fi/cwduv5idkwx9ci328nfpy/Problem...

[b] https://www.youtube.com/watch?v=Vg1p3DouX8w&

[bsoles]

I am currently reading your article with great interest as I am about to embark on implementing time series anomaly detection algorithm(s) for predictive maintenance applications. I would like to avoid unnecessarily complex algorithms that are unlikely provide real, practical benefits.

[eamonnkeogh]

Please let me know if I can help.

A paper published today finds an anomaly in an "InternalBleeding" dataset, after setting eighteen parameters [a].

Could we find the anomaly with a completely parameter-free algorithm? As the figure below shows, the answer is YES, if you use MADRID [b].

One line of code >>MADRID(UCRAnomalyInternalBleeding)

So Yes, simple is better.

[a] Learning Rate, Dropout Rate, Dim Feedforward, Batch Size, Encoder Layers, Decoder Layers, Activation Func, Time Warping, Time Masking, Gaussian Noise, Linear Embedding, Phase Type, Self Conditioning, Layer Norm, Pos. Enc. Type, FFN Layers, Window Size.

[b] https://www.dropbox.com/scl/fi/hd9gt0xs8v8mrsx3upwd3/ICDM23_...