Transition Matrix of Correlations

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Homework Help Overview

The discussion revolves around determining a probabilistic model for predicting future correlation matrices based on historical data of wave heights represented in a series of correlation matrices. The original poster seeks guidance on how to establish a model that can predict the next correlation matrix.

Discussion Character

  • Exploratory, Conceptual clarification, Assumption checking

Approaches and Questions Raised

  • Participants explore the dependency of the correlation matrices on time and previous matrices. There are questions about whether the matrices are influenced solely by the last time value or by all previous matrices. The original poster mentions identifying periodic trends in the data and visualizing the matrices as a sequence of frames.

Discussion Status

Some participants have provided insights regarding the modeling approach, emphasizing the need to consider the underlying data and its characteristics. The original poster has shared additional context about their observations, indicating a productive exchange of ideas, though no consensus has been reached.

Contextual Notes

The original poster has noted the presence of periodic trends in their historical data and has attempted to visualize the matrices in a way that reflects their temporal dependencies. There may be constraints related to the specific nature of the data and the assumptions that can be made about it.

tonino1984
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Homework Statement


Hi all,
I've the following problem: I've a series of correlations matrices, suppose 15x15 matrix of correlations between waves heights. Giving an historical series of correlation values, how can I determine a model to establish that the next correlation matrix has a probabilistic value to be in a determinate state?

Thanks for your help


Homework Equations





The Attempt at a Solution

 
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Sorry for the bad explanation, I'm trying to do better:
suppose you have a matrix doing like this
(0.2 0.3 0.4)
(0.1 0.1 0.5)
(0.4 0.4 0.1)
at time T0, where at time T1 it changes in
(0.1 0.6 0.2)
(0.4 0.2 0.1)
(0.3 0.2 0.7)

Suppose I've a lot of this hist matrices, how can I determine that probabilisticly the T(n) matrix will be
(0.6 0.7 0.8)
(0.1 0.1 0.5)
(0.4 0.4 0.1)
ie.

Thanks
 
Hi there,
any suggestion?
 
This seems to be kind of a wide-open question. I mean, do you think that the matrices are dependent on time? Are they dependent on only the matrix from the previous time value? On all the previous matrices?

From a modeling standpoint, it seems like all you have is data. There isn't really a reason to assume a priori that any particular model is right, unless you know something extra about the underlying data. So I think you have to use your knowledge about the particular process here in order to formulate a model to predict T(n).
 
hgfalling said:
This seems to be kind of a wide-open question. I mean, do you think that the matrices are dependent on time? Are they dependent on only the matrix from the previous time value? On all the previous matrices?

From a modeling standpoint, it seems like all you have is data. There isn't really a reason to assume a priori that any particular model is right, unless you know something extra about the underlying data. So I think you have to use your knowledge about the particular process here in order to formulate a model to predict T(n).

Hi hgfalling, thanks for your reply. I've extrapolated several series from those matrix, ie a(1) at T0 then a(1) at T1 etc. to obtain an historical series, and I found that there's a sort of periodical trend in most series, like harmonical series. In fact it's a matrix of waves height, so it's what I supposed to find out. To better understand that model I plot this as "chess board", and I think about this as a set of frames that compose a sort of movie, so I think that every matrix which represents a status is dependent from the previous. In your opinion how can I find out the Tn+1 matrix?

Hope to be more understandable in this tread.

Thx :-)
 

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