Characteristics of Prediction Models

Prediction models are quite useful in many fields like weather forecasting, demand and supply studies, production planning and control, project management, etc.

Here are some of the essential characteristics of a prediction model:

• Prediction models should be able to provide the value of a a parameter before it occurs. Example: the model should be able to tell which team will win the Cricket world cup before the world cup starts till the final match reaches a conclusive stage.
• Prediction models shouldn't just provide the %times the actual value will be within a certain range (or outside as the case may be) but what the range of values in a particular case could eventually be. This is like telling a patient that 20% of patients who get admitted to the hospital don't leave alive. For a particular patient though it will be important to know what is %chance of survival for him or her given the health condition at the time of admission. Example: the model instead of telling that India will win since it has won 2 world cups in the past should instead tell that given India has won last five ODI, series which is more than all the other teams, India has 80% chance of winning the world cup.
• Prediction models should provide the prediction either in an ordered manner with %likelihood of occurrence, or a set of outcomes in a narrow range or a set of few values or at best a single value (in the case of a single value the prediction model is no less than God if the prediction comes to be true in 100% cases). Example: the model should tell that India will win with 80% probability and Australia will win with 40% probability, or one of India or Australia will win it, or India will win it.
• Prediction models should be able to dynamically adjust the prediction made in a progressive manner continuously or at least at multiple interim points till the actual value occurs. This point hides a weakness of prediction models based on historical data - if something happens that has never happened in the past the prediction model may fail to adjust the prediction. Example: In case India looses first 3 matches the model may change the prediction to Australia or maybe some other team winning?
• Prediction models should have some predictors that can be controlled or manipulated to get a desired outcome in case the original prediction is not a desired one. If control or manipulation is not possible then the prediction should be available early enough to plan for appropriate contingency actions. Example: the model should be able to tell that while on the way to airport to catch a flight one would get delayed if one goes by road in which case one can go by train instead. in the case where control or manipulation is not possible like hurricane in an area (which cannot be controlled) the prediction should be available early enough to inform everyone staying in that area to move to another area.