[Numpy-discussion] recommendations on goodness of fit functions?

[Brennan Williams]

Charles R Harris wrote:
>
>
> On Mon, Apr 13, 2009 at 6:28 PM, Brennan Williams 
> <brennan.williams at visualreservoir.com 
> <mailto:brennan.williams at visualreservoir.com>> wrote:
>
>     Hi numpy/scipy users,
>
>     I'm looking to add some basic goodness-of-fit functions/plots to
>     my app.
>
>     I have a set of simulated y vs time data and a set of observed y
>     vs time
>     data.
>
>     The time values aren't always the same, i.e. there are often fewer
>     observed data points.
>
>     Some variables will be in a 0.0...1.0 range, others in a
>     0.0.....1.0e+12
>     range.
>
>     I'm also hoping to update the calculated goodness of fit value at each
>     simulated timestep, the idea being to allow the user to  set a
>     tolerance
>     level which if exceeded stops the simulation (which otherwise can keep
>     running for many hours/days).
>
>
Before I try and answer the following, attached is an example of a 
suggested GOF function.

> Some questions.
>
> 1) What kind of fit are you doing?
> 2) What is the measurement model?
> 3) What do you know apriori about the measurement errors?
> 4) How is the time series modeled?
>
The simulated data are output by a oil reservoir simulator.

Time series is typically monthly or annual timesteps over anything from 
5-30 years
but it could also be in some cases 10 minute timesteps over 24 hours

The timesteps output by the simulator are controlled by the user and are 
not always even, e.g. for a simulation over 30 years you may
have annual timesteps from year 0 to year 25 and then 3 monthly from 
year 26-29 and then monthly for the most recent year.

Not sure about measurement errors - the older the data the higher the 
errors due to changes in oil field measurement technology.
And the error range varies depending on the data type as well, e.g. 
error range for a water meter is likely to be higher than that for an 
oil or gas meter.
I'll try and find out more about that.

Brennan

> Chuck
>
>
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