[time-nuts] Long Wave Radio-Frequency standard testing

[Bob kb8tq]

Hi

The normal approach to filling a gap is to put in a point that is the average
of the two adjacent points. The assumption is that this is a “safe” value that
will not blow up the result. That’s probably ok if it is done rarely. The risk is
that you are running a filter process (averaging is a low pass filter). 

If you pull out a *lot* of outliers and replace them, you are doing a lot of filtering.
Since you are measuring noise, filtering is very likely to improve the result. 
The question becomes - how representative is the result after a lot of this or 
that has been done? 

Obviously the answer to all this depends on what you are trying to do. If you
are running a control loop and the output improves, that’s fine. If you are 
trying to provide an accurate measure of noise …. maybe not so much :) 

Bob

> On Jan 19, 2021, at 2:15 AM, Gilles Clement <clemgill at gmail.com> wrote:
> 
> Hi, 
> Yes outliers removal creates gap in Stable32.
> The « fill »  function can fills gaps with interpolated values. 
> It does not change much the graphs, except in the low Tau area (see attached). 
> Do you know a discussion of impact of outliers removal ? 
> Gilles. 
> 
> 
> 
>> Le 18 janv. 2021 à 22:06, Bob kb8tq <kb8tq at n1k.org> a écrit :
>> 
>> Hi
>> 
>> As you throw away samples that are far off the mean, you reduce the sample
>> rate ( or at least create gaps in the record). Dealing with that could be difficult.
>> 
>> Bob
>> 
>>> On Jan 18, 2021, at 1:33 PM, Gilles Clement <clemgill at gmail.com> wrote:
>>> 
>>>> Hi
>>>> 
>>>> Very cool !!!
>>>> 
>>>> The red trace is obviously the one to focus on. Some sort of digital loop that
>>>> only operates under the “known good” conditions would seem to make sense. 
>>>> 
>>>> Thanks for sharing 
>>>> 
>>>> Bob
>>> 
>>> Hi,
>>> I tried something with the idea to consider night records fluctuations as « outliers » as compared to day records. 
>>> Indeed the 3 days record mean value is flat and the histogram quite gaussian. 
>>> So I processed the 3 days record (green trace) with Stable32’s « Check Function »,
>>> while removing outliers with decreasing values of the Sigma Factor. The graph below shows the outcome. 
>>> The graph with Sigma=0.8 (blue trace) connects rather well with the 1Day record (red trace). 
>>> Would this be a workable approach ? 
>>> Best, 
>>> Gilles. 
>>> 
>>> 
>>> 
>>> 
>>> 
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