Hi Sean,

I'm not sure if you still remember me, but back when I was working on my bachelor's thesis, I spent quite some time studying matrix profiles for multidimensional motif discovery. We had several discussions about the approach, and I also tried to contribute a bit to the implementation.

In the meantime, I have completed my master's thesis, though I still need to finish one last project before officially graduating. I started this project alongside my work as a student assistant at the Fraunhofer Institute. The goal is to compare different anomaly detection methods on time series data. For this project, I have been diving deep into machine learning methods, and the process has been dragging on because I've frequently encountered questionable results and inconsistencies in many ML approaches. Interestingly, a bachelor’s student at our university is facing similar issues, and our professor has even suggested writing a paper about the problems and questionable practices we've come across.

Long story short: while writing to my professor about this, I came across the comparatively new paper from the matrix profile group on multidimensional time series analysis, and it piqued my curiosity. I immediately checked the STUMPY repository to see if anything has been implemented yet and found this issue.

Have you already had a chance to take a closer look at the paper or perhaps implemented anything from it? I haven't read the paper in detail yet, but I'm quite interested, mainly because I always found the matrix profile papers to be of much higher quality than most of the work I've encountered in the machine learning field so far.

Also, from what I've seen, most ML-based anomaly detection methods tend to identify points in time where anomalies occur, but they rarely provide concrete information about in which dimensions or how many dimensions the anomalies manifest. This leads exactly to the kinds of problems the matrix profile group described: when searching across all dimensions simultaneously, there is a risk of missing individual motifs (or anomalies), while treating each dimension separately can ignore important interdependencies between dimensions that are critical for identifying anomalies.

Best regards,
Sarah

Multidimensional Anomaly Detection #1030

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