Forecasting
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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. Forecasting is commonly used in discussion of time-series data.[1]
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.
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Categories of forecasting methods
Time series methods
Time series methods use historical data as the basis for estimating future outcomes.
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 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 methods
Judgemental forecasting methods incorporate intuitive judgements, opinions and probability estimates.
- Composite forecasts
- Surveys
- Delphi method
- Scenario building
- Technology forecasting
- Forecast by analogy
Other methods
Forecasting accuracy
The forecast error is the difference between the forecast value and the actual value for the corresponding period.
<math>\ E_t = Y_t - F_t </math>
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) | E_t|}{N} </math> |
| Mean Absolute Percentage Error (MAPE) | \frac{E_t}{Y_t}|}{N} </math> |
| Mean squared error (MSE) | <math>\ MSE = \frac{\sum_{t=1}^N {E_t^2}}{N} </math> |
| Root Mean squared error (RMSE) | <math>\ RMSE = \sqrt{\frac{\sum_{t=1}^N {E_t^2}}{N}} </math> |
See also
Application of forecasting
Forecasting has application in many situations:
- Weather forecasting and Metereology
- Transport planning and Transportation forecasting
- Economic forecasting
- Technology forecasting
- Earthquake prediction
- Land use forecasting
- Product forecasting
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.
- Kress, George J., Snyder, John (30 May 1994). Forecasting and market analysis techniques: a practical approach (in English). Westport, Connecticut, London: Quorum Books. ISBN 089930835X.
- Geisser, Seymour (1 June 1993). Predictive Inference: An Introduction (in English). Chapman & Hall, CRC Press. ISBN 0412034719.
See also
External links
- The International Institute of Forecasters
- Forecasting Principles: "Evidence-based forecasting"
- Long-Range Forecasting: "The accumulated wisdom of ages... about forecasting methods"
- Principles of Forecasting Handbook
- http://www.statsoft.com/textbook/sttimser.html
Categories
Estimation theory | Statistics