Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods. Usage can differ between areas of application: for example, in hydrology, the terms “forecast” and “forecasting” are sometimes reserved for estimates of values at certain specific future times, while the term “prediction” is used for more general estimates, such as the number of times floods will occur over a long period.
Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be up to date in order for the forecast to be as accurate as possible.
Categories of forecasting methods
Qualitative vs. quantitative methods
Qualitative forecasting techniques are subjective, based on the opinion and judgment of consumers, experts; they are appropriate when past data are not available. They are usually applied to intermediate- or long-range decisions. Examples of qualitative forecasting methods are informed opinion and judgment, the Delphi method, market research, and historical life-cycle analogy.
Quantitative forecasting models are used to forecast future data as a function of past data; they are appropriate when past data are available. These methods are usually applied to short- or intermediate-range decisions. Examples of quantitative forecasting methods are last period demand, simple and weighted N-Period moving averages, simple exponential smoothing, and multiplicative seasonal indexes.
Native Approach
Naïve forecasts are the most cost-effective forecasting model, and provide a benchmark against which more sophisticated models can be compared. For stationary time series data, this approach says that the forecast for any period equals the historical average. For time series data that are stationary in terms of first differences, the naïve forecast equals the previous period’s actual value.
Tim Series Methods
Time series methods use historical data as the basis of estimating future outcomes.
- Moving average
- Weighted moving average
- Kalman filtering
- Exponential smoothing
- Autoregressive moving average (ARMA)
- Autoregressive integrated moving average (ARIMA) e.g. Box-Jenkins
- Extrapolation
- Linear prediction
- Trend estimation
- Growth curve (statistics)
Causal / econometric forecasting methods
Some forecasting methods try to identify the underlying factors that might influence the variable that is being forecast. For example, including information about climate patterns might improve the ability of a model to predict umbrella sales. Forecasting models often take account of regular seasonal variations. In addition to climate, such variations can also be due to holidays and customs: for example, one might predict that sales of college football apparel will be higher during the football season than during the off season.
Several informal methods used in causal forecasting do not employ strict algorithms, but instead use the judgment of the forecaster. Some forecasts take account of past relationships between variables: if one variable has, for example, been approximately linearly related to another for a long period of time, it may be appropriate to extrapolate such a relationship into the future, without necessarily understanding the reasons for the relationship.
Causal methods include:
- Regression analysis includes a large group of methods for predicting future values of a variable using information about other variables. These methods include both parametric (linear or non-linear) and non-parametric techniques.
- Autoregressive moving average with exogenous inputs (ARMAX)
Quantitative forecasting models are often judged against each other by comparing their in-sample or out-of-sample mean square error, although some researchers have advised against this.
Judgmental methods
Judgmental forecasting methods incorporate intuitive judgements, opinions and subjective probability estimates.
- Composite forecasts
- Cooke’s method
- Delphi method
- Forecast by analogy
- Scenario building
- Statistical surveys
- Technology forecasting
Artificial intelligence methods
- Artificial neural networks
- Group method of data handling
- Support vector machines
Often these are done today by specialized programs loosely labeled
- Data mining
- Machine Learning
- Pattern Recognition
Other methods
- Simulation
- Prediction market
- Probabilistic forecasting and Ensemble forecasting
- Some socioeconomic forecasters often try to include a humanist factor. They claim that humans, through deliberate action, can have a profound influence on the future. They argue that it should be regarded a real possibility within our current socioeconomic system that its future may be influenced by, to a varying degree, individuals and small groups of individuals. Recent popular publications like Capital in the Twenty-First Century are regarded as major contributors to the increasingly apparent possibility of such reality. It is argued that the influence private and public investment have on our future can never be discomposed of the individual Machiavelian human character. All methods that disregard this factor can not only never accurately predict our socioeconomic future, but can even be used as strong coercion tools. Such theories are generally regarded conspiracy theories, but the increasingly worrying socioeconomic development in the world grants some of these theories a persistent credibility.
Types of Forecasting Method
The type of forecasting method you select depends on the nature of your item. Are there seasonal trends? Is demand steady, cyclical or sporadic? Are trends strong or limited? Is the item new? Because each item you forecast has a different history (and future), you should select a method most appropriate to each item. A forecasting method that fits well for one data set might be inaccurate for another item.
Deciding which forecasting method to select can be challenging, especially across a large product line and using only spreadsheets. Sophisticated forecasting software can test multiple methods for each item and determine which method will give you the most accurate results.
