Introduction to Forecasting
Forecasting is a crucial process for several industries and is an essential process for financial institutions. All procedures that over the years have been developed to address the problem of forecasting have several elements in common:
- Underlying uncertainty
- Eventually, these procedures are always wrong
- Always have a purpose or objective
There are several approaches to forecasting:
- The time-series approach is the one taken by IPredict and is essentially based on the idea that history repeats itself, at least approximately. It is generally based on the numerical series of the quantity you’re trying to predict.
- The causal approach assumes that there is a reason behind the patterns you’re seeing. You must understand the reason and use that knowledge to generate the forecast.
- The judgmental approach is simple, it assumes that someone else knows and can tell you the right answer. You essentially have to gather the knowledge and opinions of other people who are in a position to know what the demand will be, what the price will be, what the whatever quantity we’re predicting will be.
- The experimental approach is useful when we’re dealing with new items and we have no other information upon which to base a forecast. You conduct an experiment on a small group of customers and then extrapolate the results to a larger group.
Although all approaches have been used in the past for the purpose of forecasting, the time series approach is the one most widely used for forecasting, partially because it is “anonymous” i.e. not biased by the judgment required in judgmental, causal and experimental approaches, partially because it is based on no assumptions (right or wrong) on the world you’re trying to model and partially because they are easy to use with modern computers.