The first step in the process is Gorie definition.
This basically means that we should be clear about our objective data mining and clearly defining the
forecasting goal is essential for arriving at useful results.
Here is a list of some of the questions that you must answer before starting the analysis.
You must first determine the purpose of generating forecasts.
The type of forecasts that are needed.
How the forecasts will be used by the organization.
What are the costs associated with forecast errors?
What data will be available in the future, etc. in the next few slides?
I'm covering some of the important considerations during the goal setting step.
Pay attention because these issues will affect every step in the forecasting process from data collection
through data processing, modeling and performance evaluation.
First comes whether our goal is descriptive analysis or predictive analysis.
Time series data modeling, as done for either descriptive or predictive purposes in descriptive modeling
or time studies, is modelled to determine its competence in terms of seasonal patterns or trends or
relation to external factors and the like.
These can then be used for decision making and policy formulation.
For example, if you want to find out the effect of rainy season on travel bookings, that is descriptive
analysis.
In contrast, predictive analysis uses the information in a time cities to forecast future values of
parties.
For example, if you want to find out the number of travel bookings that will be done in the coming
three months, that is predictive analysis.
The difference between descriptive and predictive goals leads to the differences in the types of methods
used and in the modeling process itself.
For example, in selecting a method for describing a time cities or even for explaining its patterns,
priority is given to methods that produce explainable results.
And two models which are based on causal arguments, on the other hand, are predictive model is judged
by its predictive accuracy rather than its ability to provide correct explanation.
In short, for descriptive analysis, those models are preferred, which are more explainable, and
for predictive analysis, we prefer those models which give higher accuracy, even if they are complex
and not easily explainable.
Let's consider an example.
To understand this more clearly, Amtrak is a U.S. railway company which routinely collects data on
ridership.
In this example, we will look at these series of monthly Amtrak ridership between January 1991 and
March 2004 in the United States.
This dataset is part of the course resources, not what could be the different analysis goals for a
railway company.
Let's see.
One possible analysis goal that Amtrak might have is to forecast future monthly ridership on its trains
for the purpose of pricing.
Clearly, this is a predictable.
Setting the prices as per demand is a popular practice by airlines and hotel chains.
You may have noticed hotel and flight prices are higher in a peak travel season as compared to other
season.
This strategy of pricing is called revenue management.
Anyway, predicting future ridership is a predictable or different goal for which Amtrak might want
to use these ridership data is for the impact assessment or for evaluating the effect of some, even,
for example, airport closure due to bad weather or opening of a new large national highway.
This goal is retrospective in nature.
That is airport was closed probably last month and we are trying to establish the impact of that event
on rail ridership.
Now, this goal is therefore descriptive as it will describe the impact of that event.
This analysis would compare the series before and after the event, and no direct interest is shown
in the future values of the series.
With these two examples, I hope that you can clearly differentiate between descriptive and predictive,
good predictive analysis is also called Time series forecasting, whereas descriptive analysis is called
times in his analysis.
The focus in this course is on time to these forecasting, where the goal is to predict future values
of the times it is.
However, some of the methods that we will take, you can also be used for descriptive purposes.