ok - good to know. Thank you.
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Thank you!
On the multivariate side, I can definitely see a need for comparing the performance of different graph theory/network measures. There are a variety of different ways to both define connectivity (e.g., correlation, coherence, measures based on phase, directed measuresā¦) and evaluate various network properties, such as node centrality (node strength, eigenvector centrality, participation coefficient, etc.). As you noted in your tutorial for other measures, most papers use only a few definitions of connectivity and a few network measures, at most, making comparisons difficult (Iām guilty of this practice, myself!). Additionally, there is a lot of overlap in the information that different measures capture, as well as a need to compare the performance of these network measures to simpler, univariate ones.
Out of curiosity, are you planning to include network analysis in your multivariate approach, or are you aware of any other researchers who are undertaking a similar comparative approach in this domain?
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Thanks @gmschroe ā yes, what you describe is exactly what we are in the process of developing at the moment! The closest thing to it that exists is work done by a colleague of mine back during my PhD, who took a similar philosophy to the network analysis literature (High throughput network analysis), but it somehow was never taken to completion/published other than this brief article: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.702.955&rep=rep1&type=pdf#page=5
By next year we should have a full framework enabling comparison on all aspects of statistics you can derive from a multivariate time-series dataset (and this one will be in python) 
Do follow up (e.g., by email) if you have specific applications in mind from your own work
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Thanks @sanjayankur31,
All of our reduced sets are coded in C, with wrappers for any language, and the new multivariate version of hctsa that weāve been developing now is being done completely in python.
Perhaps they donāt get in touch with me (or more likely donāt know about hctsa), but Iāve only ever heard from one University researcher who wanted to use hctsa but didnāt have Matlab access (from an Austrian research institute), which makes it hard to distinguish whether this is a major issue for researchers in practice. But I take the principle of your point (and this is much clearer now than it was 12 years ago when hctsa development began) which is why new software developed in our lab is done in an open source environment (python so far).
The catch22 repo contains Matlab code, but this is only for testing the C coded implementations against the original Matlab files, its functionality is based on C files with instructions to compile and run from R, python, ā¦ (i.e., the Matlab files in the repo are not required for the functionality of computing the 22 features in any environment).
hctsa may be runnable in Octave (it used to be some years ago when that Austrian researcher worked with it, cf. pyopy, but with such a reduced feature set given the lack of access to toolboxes etc.) that Iām not sure itās a good substitute. The interesting part of hctsa is that it only needs temporary access to matlab (for the initial computing of features), after which you can export the results to .csv, say, and determine the features that are useful for you and recode them as needed. We hope that through this process, we will start accumulating efficiently coded highly performing features in the spirit of the catch22 feature set (we are currently developing more such sets).
Thanks for your comments and feedback
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