In this video, we will be learning about white noise.
We are learning about white noise, because if the series that we want to forecast that cities is white
noise, then we cannot predict that cities.
We cannot forecast that RTD white noise basically means that it is a sequence of random numbers and
we cannot forecast random numbers.
So in this video, we're going to learn about some techniques which we can use to identify whether the
series is white noise or not.
We will do this for two reasons.
Firstly, we will try to see whether our cities to be forecasted is Vytenis or not.
If it is white noise, it cannot be predicted and we will stop the process there only if it is not white
noise.
We will do the predictions, find out the errors in our predictions.
That is, we'll find out the difference between actual value and the predicted values.
And then we will see if these error values are white noise or not.
If these air values are not white noise, then this means that there is still information which could
have been modelled.
But our current model was not able to take out that information in such a case.
We may look for some more advanced forecasting model.
Whereas if the forecast errors are white noise, it means that all information has been hardness and
all that is left is random fluctuations.
So in somebody's.
Our cities should not be white noise so that we can model it and forecast it and our errors should be
white noise so that the model that we are using is capturing all the information in our cities.
With this background, let us look at the tools which are used to identify whether a C.D. is a white
noise or not.