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Here are the general's steps in Malta in the process of forecasting in the coming lectures.

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We will focus more on each of these steps.

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But in this lecture, we will just look at the overall process as an all data analysis.

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The process of forecasting begins with good definition, which means that we need to clearly set the

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objectives which we aim to achieve from the forecasting exercise.

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Then relevant data is collected and cleaned and explored using visualization tools.

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This is basically getting the data ready for analysis.

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And this is a big task.

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In fact, 50 to 60 percent of your time will be spent in getting the data ready for analysis.

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Once the details are ready, we select a set of prudential forecasting methods based on the nature of

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the data.

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All these different methods are applied and their performance is compared in terms of forecast accuracy

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and other measures related to what set good, the best method is then chosen.

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And it is used to generate forecast.

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Of course, the process does not end here because forecasting is typically an ongoing goal and forecast

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accuracy is monitored and sometimes the forecasting method is adapted or changed to accommodate the

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changes in the good or changes in the data over time.

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Also note the two set of arrows.

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These are indicating that part of the process are iterative.

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For instance, once the series is explored, one might determine that the series at hand cannot achieve

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the required goal leading to the collection of new or supplemented data.

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And that I dreaded process takes place here.

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When we are playing a forecasting method and evaluating its performance, the evaluation often leads

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to tweaking or adapting the existing matter or even trying out some new method.

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This is the process generally followed for forecasting.

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Keeping this process low in mind will help you approach the problem at hand in a structured way.

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Now, let's start discussing these tips in depth.

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One by one.