Determining the autocorrelation function

In summary, the author is unsure of how to solve a problem involving the WSS process. He asks for help and is given a summary of the content. He is confused by having the equation be in terms of x when in this case it is in terms of s. He is correct in thinking that he has to calculate the product of the mean of S(t) and S(t + T).
  • #1
L.Richter
21
0

Homework Statement


Stress described as:

S(t) = a0 + a1X(t) + a2X2(t)

where X(t) is a the random displacement, a Gaussian random process and is stationary.

Determine the autocorrelation function of S(t) (hint: remember a nice formula for the evaluation of high order moments of Gaussian random variables).

Homework Equations



Rxx(T) = E[X(t)X(t+T)]

The Attempt at a Solution



I am unsure of how to approach the problem using the above equation. Please advise. I can do the math I just need to see the setup with the proper functions plugged in.
 
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  • #2
Maybe you are confused by having the equation be in terms of x when in this case it is in terms of s?
[tex]R_{SS}(\tau)= E\{S(t)S(t+\tau)\}[/tex]
 
  • #3
Am I correct in thinking that I have to calculate the product of the mean of S(t) and S(t+T)?

Also since the X(t) is stationary, can I assume that S(t) is also stationary and that the expectation would be S(t)2?
 
  • #4
L.Richter said:
Am I correct in thinking that I have to calculate the product of the mean of S(t) and S(t+T)?

Also since the X(t) is stationary, can I assume that S(t) is also stationary and that the expectation would be S(t)2?

You are correct in that you must calculate the mean of S(t) times S(t + T). You are also correct that S(t) is stationary. You are incorrect, however, in thinking you would compute the expectation of S(t)2. A WSS process will have an autocorrelation that is a function of a time difference. Computing the expectation of S(t)2 would give you the autocorrelation of S evaluated at a time difference of zero. You want a function for all time differences. The time difference is T the way you write it.

Insert the definition of S(t) into that expectation. Distribute the terms on each other. Use the linearity of the expectation operator.
 
  • #5
Thank you so much for your help!

This is part 4 of one problem. I still have 2 more complete problems to do! I will keep you in mind for any further help, if that's ok.
 

1. What is the autocorrelation function?

The autocorrelation function is a statistical tool used to measure the linear relationship between a time series and its lagged values. It is a measure of how a series is correlated with itself at different time intervals.

2. Why is determining the autocorrelation function important?

Determining the autocorrelation function is important because it allows us to analyze the patterns and trends in a time series. It helps us understand the degree of dependence between observations at different time points and can be used to make predictions about future values.

3. How is the autocorrelation function calculated?

The autocorrelation function is calculated by first calculating the correlation coefficient between the time series and its lagged values at different time intervals. This is done for a range of lag values, and the resulting values are plotted on a graph to show the strength and direction of the correlation.

4. What do positive and negative values of the autocorrelation function indicate?

A positive value of the autocorrelation function indicates a positive linear relationship between the time series and its lagged values, meaning that as one increases, the other tends to increase as well. A negative value indicates a negative linear relationship, meaning that as one increases, the other tends to decrease.

5. How can the autocorrelation function be used in time series analysis?

The autocorrelation function can be used in time series analysis to identify patterns and trends, detect seasonality, and check for the presence of a unit root (a stochastic trend) in the data. It can also be used to determine the appropriate order of autoregressive and moving average terms in time series forecasting models.

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