Don’t make this mistake when clustering time series data (towardsdatascience.com)

My intuition is that clustering subsequences of time series data should easily find diurnal/seasonal patterns - am I thinking about this wrong?

Keogh has produced a bunch of interesting work. Toward Parameter Free Data Mining is a gem: https://www.cs.ucr.edu/~eamonn/SIGKDD_2004_long.pdf Of course SAX is also super interesting: https://www.cs.ucr.edu/~eamonn/SAX.pdf

Just to give some context, the paper referenced by the post[0] is a bit old. Eamonn Keogh is (to my knowledge) the current head of the research group that developed the Matrix Profile, which purports to be useful in clustering[1]. Actually on their main introduction to the MP[2] they use a chart to explain motif discovery and chains that is very similar to the one on section 6 of [0].

To be fair, I don’t remember any of the papers of the group explicitly discussing subsequence clustering, but one hopes they have advanced on that front.

[0] https://www.cs.ucr.edu/~eamonn/meaningless.pdf

[1] https://www.cs.ucr.edu/~eamonn/MatrixProfile.html

[2] https://www.cs.ucr.edu/~eamonn/Matrix_Profile_Tutorial_Part1...

That title seems awfully clickbaity to me.

How about something with some meat on its bones, like "Sliding windows on time series subsequences yields meaningless clusters"?