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.
- 1 Categories of forecasting methods
- 2 Forecasting accuracy
- 3 Seasonality
- 4 Applications
- 5 Limitations
- 6 See also
- 7 References
- 8 External links
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.
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.
Time 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
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 [clarification needed], 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.
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
Often these are done today by specialized programs loosely labeled
- 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.
The forecast error is the difference between the actual value and the forecast value for the corresponding period.
where E is the forecast error at period t, Y is the actual value at period t, and F is the forecast for period t.
Measures of aggregate error:
|Mean absolute error (MAE)|
|Mean Absolute Percentage Error (MAPE)|
|Mean Absolute Deviation (MAD)|
|Percent Mean Absolute Deviation (PMAD)|
|Mean squared error (MSE) or Mean squared prediction error (MSPE)|
|Root Mean squared error (RMSE)|
|Forecast skill (SS)|
|Average of Errors (E)|
Business forecasters and practitioners sometimes use different terminology in the industry. They refer to the PMAD as the MAPE, although they compute this as a volume weighted MAPE. For more information see Calculating demand forecast accuracy.
- Calculating demand forecast accuracy
- Consensus forecasts
- Forecast error
- Prediction intervals, similar to confidence intervals
- Reference class forecasting
It is common in many situations – such as grocery store or even in a Medical Examiner’s office -- that the demand depends on the day of the week. In such situations, the forecasting procedure calculates the seasonal index of the “season” – seven seasons, one for each day – which is the ratio of the average demand of that season (which is calculated by Moving Average or Exponential Smoothing using historical data corresponding only to that season) to the average demand across all seasons. An index higher than 1 indicates that demand is higher than average; an index less than 1 indicates that the demand is less than the average.
Climate change and increasing energy prices have led to the use of Egain Forecasting for buildings. This attempts to reduce the energy needed to heat the building, thus reducing the emission of greenhouse gases. Forecasting is used in Customer Demand Planning in everyday business for manufacturing and distribution companies.
While the veracity of predictions for actual stock returns are disputed through reference to the Efficient-market hypothesis, forecasting of broad economic trends is common. Such analysis is provided by both non-profit groups (including government departments and think-tanks) as well as by for-profit private institutions (including brokerage houses and consulting companies).
Forecasting has also been used to predict the development of conflict situations. Forecasters perform research that uses empirical results to gauge the effectiveness of certain forecasting models. However research has shown that there is little difference between the accuracy of the forecasts of experts knowledgeable in the conflict situation and those by individuals who knew much less.
Similarly, experts in some studies argue that role thinking[clarification needed] does not contribute to the accuracy of the forecast. The discipline of demand planning, also sometimes referred to as supply chain forecasting, embraces both statistical forecasting and a consensus process. An important, albeit often ignored aspect of forecasting, is the relationship it holds with planning. Forecasting can be described as predicting what the future will look like, whereas planning predicts what the future should look like. There is no single right forecasting method to use. Selection of a method should be based on your objectives and your conditions (data etc.). A good place to find a method, is by visiting a selection tree. An example of a selection tree can be found here. Forecasting has application in many situations:
- Supply chain management - Forecasting can be used in supply chain management to ensure that the right product is at the right place at the right time. Accurate forecasting will help retailers reduce excess inventory and thus increase profit margin. Studies have shown that extrapolations are the least accurate, while company earnings forecasts are the most reliable.[clarification needed] Accurate forecasting will also help them meet consumer demand.
- Economic forecasting
- Earthquake prediction
- Egain forecasting
- Land use forecasting
- Player and team performance in sports
- Political forecasting
- Product forecasting
- Sales forecasting
- Technology forecasting
- Telecommunications forecasting
- Transport planning and Transportation forecasting
- Weather forecasting, Flood forecasting and Meteorology
Limitations pose barriers beyond which forecasting methods cannot reliably predict.
Performance limits of fluid dynamics equations
As proposed by Edward Lorenz in 1963, long range weather forecasts, those made at a range of two weeks or more, are impossible to definitively predict the state of the atmosphere, owing to the chaotic nature of the fluid dynamics equations involved. Extremely small errors in the initial input, such as temperatures and winds, within numerical models double every five days.
Complexity introduced by the technological singularity
The technological singularity is the hypothetical emergence of superintelligence through technological means. Since the capabilities of such intelligence would be difficult for an unaided human mind to comprehend, the technological singularity is seen as an occurrence beyond which events cannot be predicted.
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- Forecasting Principles: "Evidence-based forecasting"
- International Institute of Forecasters
- Introduction to Time series Analysis (Engineering Statistics Handbook) - A practical guide to Time series analysis and forecasting
- Time Series Analysis
- Global Forecasting with IFs
- Earthquake Electromagnetic Precursor Research
- Forecasting Science and Theory of Forecasting