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I want to understand Timeseries shape similarity algorithm ( Shape-based distance aka SBD). I can't understand the statistics behind it and why it is better than DTW or other similarity measure. I'm trying to read this paper http://www1.cs.columbia.edu/~jopa/Papers/PaparrizosSIGMOD2015.pdf but can't understand anything in this regard. Any explanation with some example is really appreciated.

I started with learning about cross-correlation, normalized cross-correlation, Auto-Correlation, Normalization and Time Shift.

https://anomaly.io/understand-auto-cross-correlation-normalized-shift/

I couldn't understand the concept of sliding cross-correlation which is used to identify shape based similarity. What is "sliding" in this type of correlation and what difference it's making?

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  • $\begingroup$ Welcome to data science. You need to focus your questions. It isn't likely that someone will read the entire article for you and sum it up. Ask specific questions. $\endgroup$ – Mark.F Feb 5 '19 at 10:51
  • $\begingroup$ @Mark.F is it possible to answer now? $\endgroup$ – zubug55 Feb 6 '19 at 22:20