Now, let's discuss the Atima model are stands for auto regression, integrated moving average.
You know, about auto regression and the moving average part of it.
What does integrated stand for?
This method is called integrated because it includes the method or different thing.
You may remember different thing from previous Lichter's.
Different thing was used to be seasonal seasonals and data and data.
And I've been repeating this several times that auto regression and moving others models should not
be applied on cities, which has trend and seasonality.
So to handle Trinian's seasonality, we integrate are Almo model with the different method.
The idea's simple.
We will first remove trend using the defensing method.
We may have to do defensing several times if we are trying to remove non-linear trend.
Also, once we have been rendered, we apply auto regression on this new series to find the initial
set of forecast.
Using these initial forecast, we find it is a duals or forecast errors.
Then we apply moving average method on these days.
Dools to update our forecasts.
In the end, we reintroduce the effect of trend.
By doing these different thing that is adding Bagdadi lag values to our forecast to include deterrent
effect.
Now, when we will be applying Atima in our software, we will have to specify three parameters.
These three are parameters, letter to air, that is auto regression, ie.
That is integration.
And Emmy, that is moving average.
These are denoted by B, B and Q.
The first parameter is B.
It is also called the order of auto regression.
This basically denotes the number of blagged values we are going to use in our auto regression model.
If we are using only P and T minus one values to forecast P plus one values, then B is equal to two.
If we use only P value, then B is equal to one.
Second parameter is D..
This is the order of different thing.
This will tell our model how many times different thing is to be done to remove trend.
If we are seeing a quadratic print, we may do defensing ways, in which case B will be equal due to
bodily nutrient single.
Different thing is sufficient.
In which case we have these equal do.
One last parameter is Q which is called the order of moving average.
This is basically the window size of the desert world that we will be continuing to forecast future
residuals if we are forecasting residuals on the basis of last three residual values.
Then you is equal to three.
If we are using last two residual values, then Q is equal to two.
So by specifying these three parameters, our software will know what exactly is to be done to implement
Atima.
But how do we know what are the right values of BD and Q?
One way to know is by using our experience and understanding of the problem at hand.
For example, if we know that our product shows a quadratic print, we can straightaway said B is equal
to two.
The second way is by running a software iteration and finding which set of values of B, B and Q give
the maximum estimate accuracy.
When we do not have large data and there are limited number of options for BD and Q, this can also
be done.
So that's all about Rhema.
See you in the political class of Atima.