Here are the general's steps in Malta in the process of forecasting in the coming lectures.
We will focus more on each of these steps.
But in this lecture, we will just look at the overall process as an all data analysis.
The process of forecasting begins with good definition, which means that we need to clearly set the
objectives which we aim to achieve from the forecasting exercise.
Then relevant data is collected and cleaned and explored using visualization tools.
This is basically getting the data ready for analysis.
And this is a big task.
In fact, 50 to 60 percent of your time will be spent in getting the data ready for analysis.
Once the details are ready, we select a set of prudential forecasting methods based on the nature of
the data.
All these different methods are applied and their performance is compared in terms of forecast accuracy
and other measures related to what set good, the best method is then chosen.
And it is used to generate forecast.
Of course, the process does not end here because forecasting is typically an ongoing goal and forecast
accuracy is monitored and sometimes the forecasting method is adapted or changed to accommodate the
changes in the good or changes in the data over time.
Also note the two set of arrows.
These are indicating that part of the process are iterative.
For instance, once the series is explored, one might determine that the series at hand cannot achieve
the required goal leading to the collection of new or supplemented data.
And that I dreaded process takes place here.
When we are playing a forecasting method and evaluating its performance, the evaluation often leads
to tweaking or adapting the existing matter or even trying out some new method.
This is the process generally followed for forecasting.
Keeping this process low in mind will help you approach the problem at hand in a structured way.
Now, let's start discussing these tips in depth.
One by one.