reverse matrix profile computation

[pavlexander]

Hi,

Introduction to the problem

Currently, whether matrix profile is created - generation always starts from zero index, therefore for all data that is within the range of len(n)-m to len(n) there is no matrix profile available.. (where n is number of records in the time series). This can be further confirmed by multiple sources in documentation, such as https://stumpy.readthedocs.io/en/latest/Tutorial_STUMPY_Basics.html#Find-a-Motif-Using-STUMP

The short conclusion: data at the start of the sequence takes precedence over latter time series data.

The problem

Consider that we have streaming time series data.

As mentioned in documentation we could use stumpi with method upate() to append new data to existing time series..

From my perspective it does not make sense to use this function for the real-time data analysis because of lag that is explained in introduction.

So if you use step m that is equal to 1 day of data - this will essentially be the delay/lag. The data that we append always ends up on the end of the data-set. But we don't really see any patterns at current time t until next m data points are added.

Are my assumptions correct so far?

Question

What I would like to know is whether it's possible to reverse the matrix profile computation, so it always starts with the last index and goes backwards.

I know that I could invert the "static/frozen" data for 1-time computation, however, I don't know how to deal with real-time data (with stumpi) when patterns are needed ASAP.

TLTR Here is what I expect to see instead, is it possible? :)

It would be nice if this use-case was covered on the time series data analysis page (link above).