Distinguishing random from chaotic data

In summary, the conversation discusses the use of random numbers in computers and the distinction between chaotic data and random noise. The speaker suggests looking into cryptographically secure pseudorandom number generators and using the False Nearest Neighbors algorithm to determine the dimensionality of data.
  • #1
enricfemi
195
0
the random number we used in computer, can be accessed form some mathematical formula.

and i heard that the chaotic data is just similar with random noise.

if we just consider the data, one from compute, the other from some real process,can we distinguish it?


thx for every reply!:smile:
 
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  • #2
You should look into http://en.wikipedia.org/wiki/Cryptographically_secure_pseudorandom_number_generator" .
 
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  • #3
The phase space of a chaotic system is relatively low dimensional, while the phase space of truly random noise would be infinite dimensional.

A practical algorithm to determine this is called False Nearest Neighbors.

http://balrog.wku.edu/~amaral/docs/chaospaper/node9.html
 
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What is the difference between random and chaotic data?

Random data is a set of values that have no discernible pattern or relationship between them. Chaotic data, on the other hand, may appear to have a pattern or structure but is actually highly sensitive to initial conditions and can become unpredictable over time.

How can we distinguish between random and chaotic data?

One way to distinguish between random and chaotic data is by analyzing the data using statistical methods. Random data will likely exhibit a uniform distribution, while chaotic data may show signs of self-similarity and nonlinearity. Additionally, chaotic data may also have a positive Lyapunov exponent, indicating sensitivity to initial conditions.

Why is it important to distinguish between random and chaotic data?

Distinguishing between random and chaotic data is important in many fields, including meteorology, economics, and biology. Understanding the nature of the data can help us make more accurate predictions and identify underlying patterns and relationships.

Can data be both random and chaotic?

Yes, data can exhibit both random and chaotic behavior. For example, stock market data may have random fluctuations but also be influenced by chaotic events such as economic crises.

What are some real-world examples of random and chaotic data?

Random data can be seen in the results of a coin toss or the sequence of numbers produced by a random number generator. Chaotic data can be observed in weather patterns, population growth, and the movement of objects in a chaotic system such as the solar system.

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