Forecasting

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Forecasting is the process of estimation in unknown situations. Prediction is a similar, but more general term. Both can refer to estimation of time series, cross-sectional or longitudinal data. 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. Forecasting is used in the practice of Customer Demand Planning in every day business forecasting for manufacturing companies. The discipline of demand planning, also sometimes referred to as supply chain forecasting, embraces both statistical forecasting and a consensus process.

Forecasting is commonly used in discussion of time-series data.

Contents 1 Categories of forecasting methods 1.1 Time series methods 1.2 Causal / econometric methods 1.3 Judgemental methods 1.4 Other methods 2 Forecasting accuracy 3 Applications of forecasting 4 External links 5 See also 6 References

[edit] Categories of forecasting methods

[edit] Time series methods

Time series methods use historical data as the basis of estimating future outcomes.

• Moving average
• Exponential smoothing
• Extrapolation
• Linear prediction
• Trend estimation
• Growth curve
• Topics

[edit] Causal / econometric methods

Some forecasting methods use the assumption that it is possible to identify the underlying factors that might influence the variable that is being forecast. For example, sales of umbrellas might be associated with weather conditions. If the causes are understood, projections of the influencing variables can be made and used in the forecast.

• Regression analysis using linear regression or non-linear regression
• Autoregressive moving average (ARMA)
• Autoregressive integrated moving average (ARIMA)

e.g. Box-Jenkins

• Econometrics

[edit] Judgemental methods

Judgemental forecasting methods incorporate intuitive judgements, opinions and probability estimates.

• Composite forecasts
• Surveys
• Delphi method
• Scenario building
• Technology forecasting
• Forecast by analogy

[edit] Other methods

• Simulation
• Prediction market
• Probabilistic forecasting and Ensemble forecasting
• Reference class forecasting

[edit] Forecasting accuracy

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)
Percent Mean Absolute Deviation (PMAD)
Mean squared error (MSE)
Root Mean squared error (RMSE)

Please note that the business forecasters and demand planners in the industry refer to the PMAD as the MAPE, although they compute this volume weighted MAPE. Difference between MAPE and WMAPE is explained in Calculating Demand Forecast Accuracy

See also

• Forecast error
• Calculating Demand Forecast Accuracy
• Predictability
• Prediction interval, similar to confidence interval

[edit] Applications of forecasting

Forecasting has application in many situations:

• Supply chain management
• Weather forecasting, Flood forecasting and Meteorology
• Transport planning and Transportation forecasting
• Economic forecasting
• Technology forecasting
• Earthquake prediction
• Land use forecasting
• Product forecasting
• Player and team performance in sports
• Telecommunications forecasting

[edit] External links

• Forecasting Principles: "Evidence-based forecasting"
• Introduction to Time series Analysis (Engineering Statistics Handbook) - A practical guide to Time series analysis and forecasting
• Time Series Analysis
• Applied Forecasting: news on forecasting
• Global Forecasting with IFs

[edit] See also

[edit] References

• Armstrong, J. Scott (ed.) (2001). Principles of forecasting: a handbook for researchers and practitioners (in English). Norwell, Massachusetts: Kluwer Academic Publishers. ISBN 0-7923-7930-6. 

• Geisser, Seymour (1 June 1993). Predictive Inference: An Introduction (in English). Chapman & Hall, CRC Press. ISBN 0-412-03471-9. 

• Makridakis, Spyros; Wheelwright, Steven; Hyndman, Rob J (1998). Forecasting: methods and applications (in English). New York: John Wiley & Sons. ISBN 0-471-53233-9. 

• Kress, George J.; Snyder, John (30 May 1994). Forecasting and market analysis techniques: a practical approach (in English). Westport, Connecticut, London: Quorum Books. ISBN 0-89930-835-X. 

• Rescher, Nicholas (1998). Predicting the future: An introduction to the theory of forecasting (in English). State University of New York Press. ISBN 0791435539. 

• Turchin, P., 2007. Scientific Prediction in Historical Sociology. History & Mathematics: Historical Dynamics and Development of Complex Societies. Moscow: KomKniga. ISBN 5484010020