Forecasting

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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 the expected value for 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 judgemental 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. 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. 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. [1]

Contents 1 Categories of forecasting methods 1.1 Time series methods 1.2 Causal / econometric methods 1.3 Judgmental methods 1.4 Artificial intelligence methods 1.5 Other methods 2 Forecasting accuracy 3 Applications of forecasting 4 See also 5 References 6 External links

[edit] Categories of forecasting methods

[edit] Time series methods

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

• Rolling forecast is a projection into the future based on past performances, routinely updated on a regular schedule to incorporate data.^[1]
• Moving average
• weighted 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] Judgmental methods

Judgmental forecasting methods incorporate intuitive judgements, opinions and subjective probability estimates.

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

[edit] Artificial intelligence methods

• Artificial neural networks
• Support vector machines

[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)
Forecast skill (SS)

Please note that business forecasters and practitioners sometimes use different terminology in the industry. They refer to the PMAD as the MAPE, although they compute this volume weighted MAPE. For more information see 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 - Forecasting can be used in Supply Chain Management to make sure that the right product is at the right place at the right time. Accurate forecasting will help retailers reduce excess inventory and therefore increase profit margin. Accurate forecasting will also help them meet consumer demand.
• 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
• Political Forecasting
• Sales Forecasting

[edit] See also

[edit] References

1. ^ Rasmussen, Nils H.; Christopher J. Eichorn, Corey S. Barak, Toby Prince (2003). Process Improvement for Effective Budgeting and Financial Reporting. John Willy and Sons. p. 79. ISBN 0471455075. http://books.google.com/books?id=ZxAIxj0ZflgC&pg=PA79&dq=%22Rolling+forecast%22&client=firefox-a. 

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

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

• Gilchrist, Warren (1976) (in English). Statistical Forecasting. London: John Wiley & Sons. ISBN 0-471-99403-0. 

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

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

• Rescher, Nicholas (1998) (in English). Predicting the future: An introduction to the theory of forecasting. 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

[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
• Global Forecasting with IFs
• Earthquake Electromagnetic Precursor Research