Now we are going to talk about simple exponential smoothing in moving.
This morning, we were taking the average of some of the values of the cities.
But when we do simple exponential smoothing, we do a weighted average of the values.
The concept is that the importance of latest value will be sometimes more than the importance of order
values.
So we will assign larger weights to the latest values and smaller weights to the older ones.
Here is the formula for the same.
The new forecast where Lou is given as Alpha Times last value.
Plus, I'll find into one minus Alpha times last to last value and so on.
Hit Alpha is a constant between zero and one, and it is called smoothing constant.
Remember that we can only use moving average smoothing and exponential spudding when there is no brain
or seasonality in the city's.
The value of Alpha is to be selected by us.
If we keep Alpha close to one, then we give more importance to the decent values of the city's.
Such a model is called a fast learner because it adapt quickly with the reset values.
If you keep ULFA close to zero, then we give more importance to older values.
Such a model is called a slow learner.
So the choice of elford depends on the amount of smoothing and the relevance of history.
We want to maintain in our model when we are using a socket, we can test on multiple values of alpha
and keep the one which gives minimum error.