Hello Justin. Thank you for succinct presentation. I was wondering what is the necessity for white noise processes to adhere to the first two assumptions if their utility is overhauled through the final assumption! Effectively, the point of assuming a constant mean and variance is lost when the auto covariance is 0, no?
You gave an example of a (5,4) time period pair and a (4,3) time period pair and then stated that gives gamma (lag) 5 and 4 respectively. Aren't they each gamma (lag) 1. Although I do agree that gamma (lag) 5 and gamma (lag) 4 are 0, was the actual gamma (lag) suppose to be 1? Also, it would have been helpful, if you explained the interchange / interchangeability between past-present and present-past in the formula + examples. Still a nice video though.
The autocorrelation function (ACF) of a white noise process is a delta correlated function, which means that it is equal to zero for all lags greater than zero. The ACF of a white noise process measures the correlation between observations at different time steps. If there is no temporal correlation between any two observations, this means that the value of the ACF at any lag greater than zero should be zero. In other words, the value of the white noise process at any time step is independent of the value at any other time step. The ACF of a white noise process is not only zero for lags greater than zero, but also the ACF at zero lag is equal to 1, since the correlation between any variable and itself is always 1. This is in contrast to a random process that has temporal correlation, where the ACF will be non-zero for lags greater than zero, and it will decrease as the lag increases, showing that the correlation between observations decreases as the time lag increases.
Justin your screen is dark so it is not possible to clearly understand the writing. So I think it looks like white noise. But you are trying great congrats . Keep it up
Hello Justin. Thank you for succinct presentation. I was wondering what is the necessity for white noise processes to adhere to the first two assumptions if their utility is overhauled through the final assumption! Effectively, the point of assuming a constant mean and variance is lost when the auto covariance is 0, no?
It goes constructively, 1st is used in 2nd, 1st and 2nd used in the autocovariance one (3rd).
You gave an example of a (5,4) time period pair and a (4,3) time period pair and then stated that gives gamma (lag) 5 and 4 respectively. Aren't they each gamma (lag) 1. Although I do agree that gamma (lag) 5 and gamma (lag) 4 are 0, was the actual gamma (lag) suppose to be 1? Also, it would have been helpful, if you explained the interchange / interchangeability between past-present and present-past in the formula + examples. Still a nice video though.
Excellent
I have a question that why the autocorrelation function of white noise is a delta correlated function??? pl explain precisely..
The autocorrelation function (ACF) of a white noise process is a delta correlated function, which means that it is equal to zero for all lags greater than zero.
The ACF of a white noise process measures the correlation between observations at different time steps. If there is no temporal correlation between any two observations, this means that the value of the ACF at any lag greater than zero should be zero. In other words, the value of the white noise process at any time step is independent of the value at any other time step.
The ACF of a white noise process is not only zero for lags greater than zero, but also the ACF at zero lag is equal to 1, since the correlation between any variable and itself is always 1.
This is in contrast to a random process that has temporal correlation, where the ACF will be non-zero for lags greater than zero, and it will decrease as the lag increases, showing that the correlation between observations decreases as the time lag increases.
great
Justin your screen is dark so it is not possible to clearly understand the writing. So I think it looks like white noise. But you are trying great congrats . Keep it up
That's very strange, look fine on my end