In the last video, we learned about animal models which were able to handle train in the data.
But an obvious shortcoming of remote matter is that it is unable to handle seasonality in the data to
handle seasonality also.
We can use the method of different thing, as we have seen earlier.
We can remove seasonality by subtracting the corresponding value from people's time steps back.
That is, if we have monthly seasonality, we can subtract the previous year.
Same month sales value from this year.
Same month sales value.
This additional different in step is incorporated in a model called Satima, which stands for seasonal
auto regressive integrated moving average method.
Basically seasonal rhema.
Let's look at the parameters of Saudi model, you might be expecting that with seasonality.
Only one more parameter would be added, which will be the period of seasonality.
But that is not the case.
Seasonality, comfort for new parameters.
These are denoted as capital B, D, Q and small M.
This M is the obvious one.
The period of seasonality.
So for monthly seasonality and will be Cuil for weekly seasonality in daily data and will be saving
Beebee, you will be feeling the impact of seasonality on auto regression, integration and moving average.
Let's see how by using an example.
Suppose this is your data which has bought trend and seasonality.
So to handle Trent, you mentioned these three parameters, BDK, you bought this in places that we
do defending ones because these equal to one in this example to remove the drain on this remaining data.
We pick up to lag.
We loose because being equal to two to build other auto regressive model.
And then we build a moving average model on the last three as it was, because it was equal to three.
No.
In.
Along with these three, we will specify four more parameters.
Capital B, B, Q and smaller.
First, after removing the plane, we will do different thing again on this data for different thing.
We will use lag and value and we will do it once because we have said capitally to one.
In this example.
In the graph that I'm showing you, this is an example in which there is eyerly seasonality.
So here M is equal to Bill.
So we have done different thing with lag.
The L value to BEATTIES analyzed this data.
If you need to do double different thing for the moving seasonality, the capitally will be too.
But that is rarely the case.
Usually capitally is equal to one
after being different thing to remove the seasonality.
But also we will be making the model for this.
We will be using these two values and we'll be using the lag of seasonal values also.
So apart from lag one and lag two values.
We'll be using the lag and value.
Also, the seasonal component is giving only one input that is lagging because capital P is equal to
one.
If Capital B is equal to two, we'll be having two inputs from the seasonality.
That is lag M and lag to him since M is equal to twelve for that example.
It will be led to L value and lag guarantee for the value.
The point of doing this is that when we are predicting sales for the coming month of January, along
with these sales data of November and December, it makes sense to use these sales data of last year,
January sales also.
So this capital p value will tell how many sales values of previous years are to be taken for the auto
regression model.
Similarly, for moving average model, we will take Führer's duals from the previous seasons as well.
So this capital Q will tell how many as a dual values of same season are to be taken from the previous
years.
So that the small P.
Q and capital BD, Q and M.
We will build our study.
My model.
And another, an extension of the emo model is Sa'adi Max model, in which, apart from the normal Satima
model, which is based on the times it is, you also import some exogenous variables.
Exogenous variables will be other variables other than the variable to be predicted.
For example, if you're trying to predict the closing price of a security on the stock market, apart
from using the historical values of closing prices, you might also use variables such as open price,
volume, greater, etc. Such variables are exogenous.
Similarly, for predicting the sales of the coming days apart from using the previous day sales values,
we can use other variables, such as whether the coming day is a holiday or not, whether the coming
days are salary days or not.
Whether we are doing any promotions on those days or not and so on.
Such variables will be exogenous.
In this case, such exogenous variables can also be easily added to our model.
We just need to specify additional parameter which will contain this exogenous dataset.
Only thing that we must note is that in this additional dataset date thing should be kept as an index
and not as a separate column.
However, this part is software specific.
So for Saudi, Max, the only thing that you need to know is that Sa'adi Max model will allow you to
use other variables also and not just rely on the city's data.