
Not sure if this is widely know or even practically useful to anyone here, but I hadn't encountered this fun gizmo until the other day: https://www.cs.ucr.edu/~eamonn/PID4481997_extend_Matrix%20Profile_I.pdf
Basically, you take a short window from a time series, take the convolution of that window with another time series (or itself), take the minimum value of the convolution, and plot the minimums as you slide the window along  essentially you're plotting how close each window motif is to an existing window from the time series. Since the convolution can be computed efficiently in fourier space, with FFT this process ends up being pretty fast (apparently O(nlogn)).
Seems useful in general for signal anomaly detection. Also, it's efficient to index where (what time point) the convolution min for each window is, so you can use this detect changes in regimes (indexes will tend not to point across the change boundary). Seems like there might be other fun variations of this to play with. 



tbretagn


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Very nice find. My thesis due to some reasons had to be about graphs and I ended up extending this exact work for discord discovery (anomaly detection) in temporal graphs. As data I calculated correlated implied volatilities for dozen randomly selected stocks options over time.
Prof Keogh is very well known in time series circles and reviews papers from major research places as I heard.
I struggle to find use for it given that You are operating wo prediction part.
It seems it may be useful to find two related timeseries where one precedes the other. Potentially orderbook structure changing significantly while price didn't absorb the change yet.
Or for labeling data and building prediction engine based on (dis)similarity. 


