Forecasting: An Overview

Abstract

1 What can be forecast? Forecasting is required in many situations: deciding whether to build another power generation plant in the next five years requires forecasts of future demand; scheduling staff in a call centre next week requires forecasts of call volume; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments), or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. Some things are easier to forecast than others. The time of the sunrise tomorrow morning can be forecast very precisely. On the other hand, currency exchange rates are very difficult to forecast with any accuracy. The predictability of an event or a quantity depends on how well we understand the factors that contribute to it, and how much unexplained variability is involved. Forecasting situations vary widely in their time horizons, factors determining actual outcomes , types of data patterns, and many other aspects. Forecasting methods can be very simple such as using the most recent observation as a forecast (which is called the "na¨ıvena¨ıve method"), or highly complex such as neural nets and econometric systems of simultaneous equations. The choice of method depends on what data are available and the predictability of the quantity to be forecast. 2 Forecasting methods Forecasting methods fall into two major categories: quantitative and qualitative methods. Quantitative forecasting can be applied when two conditions are satisfied: 1. numerical information about the past is available; 2. it is reasonable to assume that some aspects of the past patterns will continue into the future. There is a wide range of quantitative forecasting methods, often developed within specific disciplines for specific purposes. Each method has its own properties, accuracies, and costs that must be considered when choosing a specific method. Qualitative forecasting methods are used when one or both of the above conditions does not hold. They are also used to adjust quantitative forecasts, taking account of information that was not able to be incorporated into the formal statistical model. These are not purely guesswork-there are well-developed structured approaches to obtaining good judgemental forecasts. As qualitative methods are non-statistical, they will not be considered further in this article.