Help with Fourier - page 11

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lsv писал (а):
shobvas wrote (a):
Give me a hint! =)
Not by the way I suspect that the solution of f(t) still includes decaying eXponents =)

At least give me a hint what direction, because I ANG3110 pestered him with questions, but it turned out in vain.
Only he and I lost time in vain =)

The decaying exponents are the same harmonic series, the problem is that this series is infinite.


If we do the Fourier transform we will get frequency series starting from f0, but in order to look into the future at least a little bit, i.e. to see the trend direction, we should make the minimum analyzed frequency to be at most 2 times less than f0 (fmin<=f0/2). But if we want to use Fourier to obtain fmin, we will have to increase the analyzed series by a factor of 2, which contradicts the condition. Conclusion: Fourier is not appropriate here. Exit: Find another algorithm, method, solution.


How about increasing the row to be annalised in this way:

for(int i=0; i<=M/2-1; i++)
{
aa[2*i]=(iClose(NULL,0,i);
aa[2*i+1]=(iClose(NULL,0,i)+iClose(NULL,0,i+1))/2;
}
In principle, you can increase this value three or four times.
 
klot писал (а):
If we increase the row to be analysed in this way:

for(int i=0; i<=M/2-1; i++)
{
aa[2*i]=iClose(NULL,0,i);
aa[2*i+1]=(iClose(NULL,0,i)+iClose(NULL,0,i+1))/2;
}
In principle, you can increase it three or four times.

And the point is, we have all the historical data in the first place. The problem is not the choice of data to be analysed, but the way the data is analysed.

 
lsv писал (а):
klot wrote (a):
And if you increase the annalizable series in this way:

for(int i=0; i<=M/2-1; i++)
{
aa[2*i]=iClose(NULL,0,i);
aa[2*i+1]=(iClose(NULL,0,i)+iClose(NULL,0,i+1))/2;
}
In principle, you can increase this value three or four times.

And the point is, we have all the historical data in the first place. The problem is not the choice of data to analyse, but the way the data is analysed.


Yes, just the smoothing of the series by Fourier methods, with such a feature is more stable.
 
klot писал (а):
Anyway, that's better :)

   //InSigNormalize(aa); //Нормализация значений 
   // Прямое преобразование Фурье - после выпонения функции в массиве aa[] - спектрограмма
   realfastfouriertransform(aa, tnn1, false); 
   InSigNormalize(aa); //Нормализация значений 
   
   //--- Вывод спектрограммы на экран
   for( i=0; i<=N-1; i++)
   {
      // Модуль комплексного числа
      SpecktrBuffer[i]=MathSqrt(aa[i*2]*aa[i*2]+aa[i*2+1]*aa[i*2+1]); 
   }

klot, why do you need normalisation before calculating the spectrum?
 
gpwr писал (а):
klot wrote (a):
Anyway, that's better :)

   //InSigNormalize(aa); //Нормализация значений 
   // Прямое преобразование Фурье - после выпонения функции в массиве aa[] - спектрограмма
   realfastfouriertransform(aa, tnn1, false); 
   InSigNormalize(aa); //Нормализация значений 
   
   //--- Вывод спектрограммы на экран
   for( i=0; i<=N-1; i++)
   {
      // Модуль комплексного числа
      SpecktrBuffer[i]=MathSqrt(aa[i*2]*aa[i*2]+aa[i*2+1]*aa[i*2+1]); 
   }

klot, why do you need normalisation before calculating the spectrum?
HAPPY NEW YEAR!!!!
I used this method to calculate the correlation of the spectra of various currencies against the dollar. In general, I am having a hard time pressing buttons right now, but I am preparing a series of articles on this method, I will publish them soon, I think many people will be interested....
In the meantime, Happy New Year !!!!!! Happy Trends to you!!!!!
 
Thank you! Happy New Year to you too!
 

to klot

Please help me understand structure of output data

realfastfouriertransform(data,N,false);

What will be the output if data=[0,1,2,3,4,5,6,7]

in matcad is

What you have in the output, you can just quote the data, I'll figure it out myself from there. Thank you. The question appeared while discussing in this forum thread'Stochastic Resonance'.

Thanks to those who helped me. Figured it out.

 
What an old thread that was!
It's a good thing I haven't read it before. It's good to be an amateur on any subject. No barriers, no preconceived notions.
PF is not suitable for prediction in a static application. This is clear as it is.
No one has raised the problem of parasitic harmonics arising from price differences at the ends of the sample.
It's a 90 degree angle!!! There are all the harmonics that exist in nature on such a front!
And almost no one has used, except klot, PF in dynamics.
I made a visualizer too. And I got an amazing result.
All that remains is to write a predictor. Of course, it won't predict far from it. But the result will be almost absolute within half of the sample.
When I get the final result I will definitely publish it. And it doesn't matter what it will be. A negative result is also a result.
 
Zhunko:
What an old thread they've picked up!
It's a good thing I haven't read it before. It's good to be an amateur on any subject. No barriers, no preconceived notions.
PF is not suitable for prediction in a static application. This is clear as it is.
No one has raised the problem of parasitic harmonics arising from price differences at the ends of the sample.
It's a 90 degree angle!!! There are all the harmonics that exist in nature on such a front!
And almost no one has used, except klot, PF in dynamics.
I made a visualizer too. And I got an amazing result.
All that remains is to write a predictor. Of course, it won't predict far from it. But the result will be almost absolute within half of the sample.
When I get the final result I will definitely publish it. And it doesn't matter what it will be. A negative result is also a result.

Now we are going to struggle with parasitic harmonics, but with a different purpose reference above. IHMO for PF price prediction is not promising to have a better matrix.
 
You have to know how to use PF in different ways.
Using it for purposes other than its intended use. I.e. the consequences of using PF in dynamics.
I got a real spectrum filter. It automatically cuts off parasitic harmonics.
I'm surprised at the result myself. I managed to turn a disadvantage of PF into an advantage.
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