John,

I think this is an example of an important general case. On both statistical 
and biological grounds I suspect you should decorrelate your inputs before 
producing SDRs from them. I haven't tried this myself (have hardly done 
anything with NuPIC) but I'm wondering if anyone has done substantial 
preprocessing to compensate for the statistics of the inputs. Even better would 
be if anyone has done a comparison of feeding NuPIC decorrelated vs. direct 
sensor inputs--or a theoretical argument that decorrelating the inputs is 
unnecessary or unwise.

  - Kevin

________________________________
From: nupic [[email protected]] on behalf of John Blackburn 
[[email protected]]
Sent: Tuesday, September 23, 2014 7:27 AM
To: Archie, Kevin
Subject: Re: Which NuPIC tutorial do you want to see next?

Hi Matthew,

Rather self serving, but I would love to see a tutorial related to the "bridge" 
simulation I've been trying to do or similar. I have not got NuPIC to work yet 
despite some effort. Basically the difference with Hotgym is we have 18 
sensors, 10 temperature and 8 tilt (ie strain) and we want to make predictions 
on all taking account of cross-correlations. So a tutorial with multiple 
correlated time series would be great!

At NPL we monitored a bridge every 5 minutes for 3 years recording all 18 
sensors so I think this data would be a great showcase for a real-world NuPIC 
example. We also perturbed the bridge at known times (cutting supports, adding 
weights etc) so we know when the anomalies should appear.

John.

On Mon, Sep 22, 2014 at 4:57 PM, Matthew Taylor 
<[email protected]<mailto:[email protected]>> wrote:
I have more tutorials planned, but I'd like some help deciding which
to do first. Please answer this 1-question poll:

https://docs.google.com/forms/d/1GBYWg_-LIaYmOz9EJ5LbFo6N2ot1xv9AA22gaNdENs0/viewform?usp=send_form

Thanks,
---------
Matt Taylor
OS Community Flag-Bearer
Numenta



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