In this video, we will discuss the moving average method of forecasting.
But no, that it is very different than the moving average moving method that we discussed earlier.
So don't confuse it with that.
Now, let's see what moving average forecasting is.
Let's say we have a time cities and we use persistance model or the auto regression model to make predictions.
I hope you remember what these two models, the persistence model, is basically using the last available
value as the forecast for next time period.
And auto regulation is running a regression model on the lagged values and using this model to predict
the future values.
So suppose you have already found out the prediction using any of these methods.
Now, let's say the actual value of the time P plus one is Y.
And the predicted value or time P plus one is Y hat.
So after applying the model on the input, we have the value y.
Another difference between Y her and the actual value Y is called residual or the forecasting at a.
We can find all the residuals where we know both the actual values and the predicted values.
Ideally, these set of residuals, that is the time series of residues should be white noise.
There should not be any pattern, indeed, residual values.
Ideally, there should not be any information left.
Indeed, as it was.
But practically, this may not always happen.
Our model may leave some information, indeed, as it was, to extract that information and improve
our forecast.
We don't not forecasting model on residuals.
We tried to forecast the future residuals.
If there is any pattern individuals, this second level model, we are calling it the second level model.
First level was on the accurate time series.
We found the residual from that.
And now we are building these taking level model on these residuals.
So if there is any pattern, indeed, residuals of second level model will identify it and capture that
information in the forecast of residues.
We can then add back these forecasted residuals to our original forecast to get better forecast.
This is the concept of moving average.
I've listed down all the steps that are used in the moving average model that is initially we use air
or persistent model to build a model.
We didn't find it as it was.
They are more along these networks and forecast these residuals.
And lastly, we use the forecastable tools to update our initial forecast.
This moving average method is often used along with auto regression.
That is, auto regression is used as the first prediction model.
And then moving averages used to update the initial predictions.
When auto regression and moving average model is used together, it is called auto momentum.
This model is quite popular, although I do remember that Ottmar can only work on cities without print
and seasonality.
So do remember to remove that before playing.
In the next lecture, we will see a variant of AAMA, which automatically handles trend and seasonality
also.
So that is all this is moving average method, if you like, residual values are used to forecast.
There's Duell for the next time period.
This forecasted residual is also ordered in the original forecast to get a new and improved forecast.
Now, let's see how to implement this in our software.