Stochastic resonance - page 15
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It looks a lot like a chaotic attractor. You're in deep,grasn...
...
It looks a lot like a chaotic attractor. You're getting in deep,grasn....
Got in deep, and what it showed was only the 'beginning'. The model of price movement in the form of a flat and a local trend as a transition from level to level is an interesting thing. Sometimes it seems to me that waves are taken from the model, but it is only a philosophy. I use attractors but not for the price but for some channel parameters.
to vaa20003
True, the amplitude and the peaks are floating a bit from day to day. Just built a sine wave with these periods and added them up. And it turns out (purely visually so far) that for every movement
is a spike or a dip. Can I use this as a sub-threshold signal?
I already stated my opinion on this matter, namely, it has no perspective. The stochastic resonance model has no prediction qualities that would make it possible to calculate the new level of a flat with respect to the market, while it seems to be of the greatest interest. But the development of a tool to control the appearance of "dangerous situations" as the possibility of a local trend and the system's transition to a new level, I think, has all the chances.
It looks a lot like a chaotic attractor. You're going deep,grasn...
I think it looks more like a shrimp, and don't laugh please :) it's not TA shrimp, it's this one http://www.ibiblio.org/e-notes/Chaos/ru/swallow_r.htm
At the end of the article "shrimp" are
(Wow, crazy pictures) Do you guys really think they are more informative than a price chart for example? Is it necessary to dig into such wilderness?
(Wow, crazy pictures) Do you guys really think they are more informative than a price chart for example? Is it necessary to dig into such wilderness?
A question to the experts, but off-topic.
Suppose there is a normally distributed sequence of values X. The number of sequence members is N=1000000, the average value is A and the ska is S. Obviously, the set of values of elements X is bounded from above, i.e. all the X's belong to the interval [0,Xmax]. We take a sample of M=100 members of the sequence and calculate its average XM. We form a new sequence Y = {XM} out of all sequential samples, containing M elements of the initial sequence. It is clear that the set of Y values is bounded too.
How to find its upper and lower bounds, i.e. the interval of [Ymin,Ymax] values ?
I'm naturally interested in the analytical evaluation by means of mathematical statistics (in which I, alas, am not strong). To calculate in head-on is not difficult, but it is not interesting. It is interesting to get the dependence of the limits of this interval on the ratio of N and M and statistical properties of the initial sequence.
A question to the experts, but off-topic.
Suppose there is a normally distributed sequence of values X. The number of sequence members is N=1000000, the average value is A and the ska is S. Obviously, the set of values of elements X is bounded from above, i.e. all the X's belong to the interval [0,Xmax]. We take a sample of M=100 members of the sequence and calculate its average XM. We form a new sequence Y = {XM} from all sequential samples containing M elements of the original sequence. It is clear that the set of Y values is bounded too.
How to find its upper and lower bounds, i.e. the interval of [Ymin,Ymax] values ?
I'm naturally interested in the analytical evaluation by means of mathematical statistics (in which I, alas, am not strong). To calculate in head-on is not difficult, but it is not interesting. It is interesting to get a dependence of the limits of this interval on the ratio of N and M and statistical properties of the initial sequence.
A little clarification, in my own words, so to speak. Did I get it right that the original sample is split into non-overlapping sections (intervals) of length M, and each sample of a new sequence is the average of data bounded by the interval, and is identified by partition numbers?
PS: not an expert at all, just wanting to help :o)
A little clarification, so to speak, in my own words. Did I get it right that the original sample is split into non-intersecting segments (intervals) of length M, and each sample of the new sequence is the average of the data bounded by the interval, and is identified by the numbers of the split?
PS: not an expert at all, just wanting to help :o)
No, it is just a sliding window of length M samples. Therefore the number of elements in the sequence Y is N-M+1.
In the limit when M=1 we get the same sequence X with its range of values [0,Xmax]. But in the opposite case M=N we get only one term in the sequence Y - the average value of the original sequence A, i.e. Ymin=Ymax=A.
The truth is always in the middle. :-) With arbitrary M 0<Ymin<A and A<Ymax<Xmax. I would like to have analytical formulas (or at least a calculation procedure) to calculate these quantities. I think that in mathemaƟcs this problem is student level and has been solved long ago.
Suppose there is a normally distributed sequence of values X. The number of members of the sequence is N=1000000, the mean value is A, and the ska is S. Obviously, the set of X element values is bounded from above, i.e. all X's belong to the interval [0,Xmax]. We take a sample of M=100 members of the sequence and calculate its average XM. We form a new sequence Y = {XM} from all sequential samples containing M elements of the original sequence. It is clear that the set of Y values is bounded too.
How to find its upper and lower bounds, i.e. the interval of [Ymin,Ymax] values ?
I'm naturally interested in the analytical evaluation by means of mathematical statistics (in which I, alas, am not strong). To calculate in head-on is not difficult, but it is not interesting. Interesting to get a dependence of the limits of the interval on the ratio of N and M and statistical properties of the initial sequence.
If X is a random variable, then Y is the sum of M independent random variables with the same distribution as X. Thus if X is normal, then Y will also be normal, with variance S/sqrt(M). The question of maximum and minimum values can only be posed for a particular realisation of the series (i.e., counts head-on), for an arbitrary realisation we can only talk about probabilities.
P.S. The above does not mean that I consider myself an expert in mathematical statistics :)
If X is a random variable, then Y is the sum of M independent random variables with the same distribution as X. Thus if X is normal, then Y will also be normal, with variance S/sqrt(M). The question of maximum and minimum values can only be posed for a particular realization of the series (i.e. counting head-on), for an arbitrary realization we can only talk about probabilities.
Of course. I meant a statistical estimate.
For example. If we know the distribution function, then for any X0 we know the probability P of occurrence of an element with value >=X0 in the sequence. If a sequence contains N elements, the total number of the elements in the sequence meeting the condition X>=X0 is P*N. If this value is less than 1, i.e. 0, then statistically Xmax<X0. But it certainly does not mean that you cannot have an element >=X0 in such a sequence.
I hope I didn't make a mistake in arithmetic anywhere ?