Forecasting


Forecasting

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.

Categories of forecasting methods

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

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

Judgmental methods

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

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

Other methods

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

Forecasting accuracy

The forecast error is the difference between the actual value and the forecast value for the corresponding period.

E_t = Y_t - F_t

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:

{N}
-
Mean Absolute Percentage Error (MAPE)
MAPE = frac{sum_{t=1}^N |frac{E_t}{Y_t}{N}
-
Percent Mean Absolute Deviation (PMAD)
PMAD = frac{sum_{t=1}^{N} |E_t{sum_{t=1}^{N} |Y_t
-
Mean squared error (MSE)
MSE = frac{sum_{t=1}^N {E_t^2{N}
-
Root Mean squared error (RMSE)
RMSE = sqrt{frac{sum_{t=1}^N {E_t^2{N

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

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
*Political Forecasting

ee also


* Calculating Demand Forecast Accuracy
* CPFR
* Foresight (future studies)
* Futures studies
* Futurology
* Global Prescience
* Optimism bias
* Planning
* Prediction
* Reference class forecasting
* Strategic foresight
* Technology forecasting

References


*cite book
author = Armstrong, J. Scott (ed.)
title= "Principles of forecasting: a handbook for researchers and practitioners
date= 2001
publisher= Kluwer Academic Publishers
location= Norwell, Massachusetts
language= English
id= ISBN 0-7923-7930-6

*cite book
author=Geisser, Seymour
authorlink =Seymour Geisser
title = Predictive Inference: An Introduction
date = 1 June 1993
publisher = Chapman & Hall, CRC Press
language = English
id = ISBN 0-412-03471-9

*cite book
author=Makridakis, Spyros
coauthors=Wheelwright, Steven; [http://www.robhyndman.info Hyndman, Rob J]
title= [http://www.robhyndman.info/forecasting/ Forecasting: methods and applications]
date=1998
publisher = John Wiley & Sons
location = New York
language = English
id= ISBN 0-471-53233-9

*cite book
author = Kress, George J.
coauthors= Snyder, John
title= "Forecasting and market analysis techniques: a practical approach
date= 30 May 1994
publisher= Quorum Books
location= Westport, Connecticut, London
language= English
id= ISBN 0-89930-835-X

*cite book
author=Rescher, Nicholas
authorlink =Nicholas Rescher
title = Predicting the future: An introduction to the theory of forecasting
date = 1998
publisher = State University of New York Press
language = English
id = ISBN 0791435539

*Turchin, P., 2007. Scientific Prediction in Historical Sociology. [http://edurss.ru/cgi-bin/db.pl?cp=&page=Book&id=53185&lang=en&blang=en&list=Found History & Mathematics: Historical Dynamics and Development of Complex Societies.] Moscow: KomKniga. ISBN 5484010020

External links

* [http://www.forecastingprinciples.com Forecasting Principles: "Evidence-based forecasting"]
* [http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4.htm Introduction to Time series Analysis (Engineering Statistics Handbook)] - A practical guide to Time series analysis and forecasting
* [http://www.statsoft.com/textbook/sttimser.html Time Series Analysis]
* [http://www.appliedforecasting.com Applied Forecasting: news on forecasting]
* [http://www.ifs.du.edu Global Forecasting with IFs]


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