How far in the future should we forecast?
Should we generate all the forecasts at a single time point?
Or can we forecast on an ongoing basis?
These are important questions to be answered at the gold definition stage.
But before we try to find these answers, let us discuss some basic notation that we are going to use.
And this.
We are going to use four types of symbols to denote time period data as forecasts and forecast errors.
So B will be used to denote the time period.
This is equal to one is the first time period indices and these equal to N means d end it.
Time period, the whity will be used to denote the actual value of the variable of interest in the pit
time period.
For example, if we start measuring daily average temperatures at the start of the week, then these
equal to one denotes day one or Monday B is equal to two, denotes day two or Tuesday and so on.
And Vivan denote the average temperature of Monday.
And why?
To denote the average temperature of Tuesday and so on.
Next is F.T..
This will be the forecasted value for time period.
Not the actual value.
Actual value is denoted by whity.
Forecasted value will be denoted by F.T..
Similarly, F.T. plus K will then denote D gay step ahead forecast when the forecasting time.
If we are currently a time Beretti, the forecast for the next time period that is D plus one is denoted
by F people as one.
So if you forecast the average temperature of Wednesday on a Monday, you are forecasting for peoples
tuart time period.
Lastly, E.T. is the forecast error for time purity.
That is it is the difference between the actual value and defocused value at 90.
So mathematically, it will be equal to Viotti minus F.T..
This is basically the inaccuracy of our forecast.
With this notation, let us denote the forecast horizon with key basically gauge the number of time
period ahead that we will be forecasting.
For example, if Gaitskell equal three four monthly data, this means that we will be forecasting for
the next three months in our Amtrak ridership.
Example, one month Ed forecast, that is if people's money will be sufficient for revenue management.
That is for flexible pricing.
Whereas longer term forecast such as dreman three months ahead forecast or F, P plus three are more
likely to be needed for procurement and inventory purposes.
Knowing forecast horizon, as does an important part of goal setting.
One of the factors that affect forecast horizon is the decency of data available.
Forecasting next month's ridership is much harder if we do not have the data for last two months.
If we do not have the data for last two months, it actually means that we are generating forecasts
of F.P. plus three rather than F.T. plus one.
Now, what is the impact of large or small forecast horizon on forecasting accuracy the further into
the future?
We are trying to forecast the more likely that the forecasting context will change and certainty will
increase.
Therefore, for larger reason, we can expect large uncertainty and less accuracy for such a situation.
We should probably examine our model and try and updated, as in when new data or information is available.
For example, if we have a three month ahead forecast for April of 2020, which we have generated in
January 2020, if possible, we should update this forecast in February and again in March of this year.
Lastly, we will discuss two points, forecasting use and level of automation.
How will the forecast be used?
Understanding how forecasts will be used, perhaps by different stakeholders is critical for generating
forecasts of the right type.
And with a useful accuracy limit, consider this question.
Does over prediction cost more or it cost less than the under prediction?
For example, if you're organizing an event and you want to forecast how many people will attend it,
if you all predict and arrange for 120 people instead of hundreds who actually turn up, there will
be some cost incurred for 20 empty seats.
On the other hand, if you under predict and arrange for 80 people only, you will lose revenue of 20
people.
And there will be some emotional loss also for those 20 people who came but were not allowed to enter.
Similarly, this question will be forecast and forecasting method is to be presented to management or
to the technical department, answer to this question will tell us the level of complexity in our model
that we can use.
Similarly, we ask, should the forecast be numerical or binary?
For example, if you're forecasting whether it will rain or not tomorrow, it is a binary forecast.
On the other hand, if you are forecasting the average temperature for the next day, that is a numerical
forecast.
Different models work for these two types of problems.
We will cover both types of models then, of course, but you must know which one is to be applied for
the problem at hand.
Next is a set of questions to determine the level of automation required in the forecasting project.
The level of required automation depends on the nature of forecasting tasks and on how the forecast
will be used in practice.
For example, when we ask how many cities need to be forecasted.
If for us in Adio there are many cities which are do we forecasted and not much forecasting expertise
in the organization.
We must automate the solution.
A classic example is forecasting at the point of sale for purpose of inventory control across many stores.
Organizations do not depend on these salesperson depuis us to manage inventory.
It is, it added, managed automatically by the system.
Several consulting firms provide such software solutions.
The question is, is default casting an ongoing process or a one time event?
If it is a one time event, we need not implement automation.
We also might want to know which daytimes software will be available during the forecasting period.
Word forecasting expertise will be available at the organization during forecasting period because lack
of expertise favours automation.
So if we do not have a lot of expertise in the organization, we may want to automate the forecasting
process so that it does not depend on deep people in the organization.
So you can see different answers will lead to different choices of data forecasting methods and evolution
schemes.
And all these questions must be answered at the gold definition stage.