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Forecasting is the process of estimation in unknown situations. Prediction is a similar, but more general term, and usually refers to estimation of time series, cross-sectional or longitudinal data. In more recent years, Forecasting has evolved into the practice of 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 consensus process.
Forecasting is commonly used in discussion of time-series data.
Categories of forecasting methodsEdit
Time series methodsEdit
Time series methods use historical data as the basis for estimating future outcomes.
Causal / econometric methodsEdit
Some forecasting methods use the assumption that it is possible to identify the underlying factors that might influence the variable that is being forecasted. 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
Judgemental forecasting methods incorporate intuitive judgements, opinions and probability estimates.
- Composite forecasts
- Delphi method
- Scenario building
- Technology forecasting
- Forecast by analogy
The forecast error is the difference between the forecast value and the actual 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
- Forecast error
- Calculating Demand Forecast Accuracy
- Prediction interval, similar to confidence interval
Application of forecastingEdit
Forecasting has application in many situations:
- Weather forecasting and Meteorology
- Transport planning and Transportation forecasting
- Economic forecasting
- Technology forecasting
- Earthquake prediction
- Land use forecasting
- Product forecasting
- 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.
- 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.
- Geisser, Seymour (1 June 1993). Predictive Inference: An Introduction (in English), Chapman & Hall, CRC Press. ISBN 0-412-03471-9.
 The main source of information about forecasting on the internet is the Forecasting Principles site, forecastingprinciples.com. Forecasting Principles summarizes all useful knowledge about forecasting for researchers, practitioners, and educators. It is provided as a public service by the International Institute of Forecasters. The Institute publishes the journals International Journal of Forecasting and Foresight, and organizes International Symposia on Forecasting and forecasting workshops.
The Institute of Business forecasting is the organization that provides seminars and conferences in demand planning and supply chain forecasting. Articles from practitioners are published by the Journal of Business Forecasting.
- The International Institute of Forecasters
- Forecasting Principles: "Evidence-based forecasting"
- Institute of Business Forecasting
- Long-Range Forecasting: "The accumulated wisdom of ages... about forecasting methods"
- Principles of Forecasting Handbook
- Applied Forecasting: news on forecasting
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