Forecasting in general is the process of making statements about events with future outcomes. According to  Hyndman and Athanasopoulos (2012) forecasting is about predicting the future as accurately as possible, given all of the information available, including historical data and knowledge of any future events that might impact the forecasts.

Forecasting is estimating in unknown situations. Predicting is a more general term and connotes estimating for any time series, cross-sectional, or longitudinal data (IIF, 2013).

The appropriate forecasting methods depend largely on what data are available. If there are no data available, or if the data available are not relevant to the forecasts, then qualitative forecasting methods must be used. These methods are not purely guesswork---there are well-developed structured approaches to obtaining good forecasts without using historical data. Examples of qualitative forecasting methods are informed opinion and judgment, the Delphi method, market research, scenario development, science and technology roadmapping methods, and historical life-cycle analogy (cf. e.g. Russell Bernard and Russell Bernard, 2012).

Quantitative forecasting methods are used to forecast future data as a function of past data. A forecasting method is an algorithm that provides a point forecast: a single vlaue that is a prediction of the value at a future time period (Hyndman et al., 2008). According to Carnot et al. (2005) quantitative forecasting can be applied when two conditions are satisfied: (a) numerical information about the past is available;(b) it is reasonable to assume that some aspects of the past patterns will continue into the future. Examples of qualitative forecasting methods are time series methods like Moving average, Weighted moving average, Kalman filtering, Exponential smoothing, Autoregressive (integrated) moving average (ARMA or ARIMA), Extrapolation, Linear prediction, Trend estimation; Artificial intelligence methods like data mining, machine learning and pattern recognition; or Simulation. For applications with R see Shumway and Stoffer (2011).


Carnot, N., Koen, V., Tissot, B. (2005). Economic Forecasting. Palgrave MacMillan New York.

Hyndman, R. J., Athanasopoulos, G. (2012). Forecasting: principles and practice. O Texts Online, Open-Access Textbooks.

Hyndman, R. J., Koehler, A. B., Ord, J. K., Snyder, R. D. (2008). Forecasting with Exponential Smoothing - The State Space Approach. Springer-Verlag Berlin Heidelberg.

International Institute of Forecasters (2013).

Shumway, R. H., Stoffer, D. S. (2011). Time Series Analysis and Its Applications. With R Examples. Springer Science+Business Media New York.

H. Russell Bernard, Harvey Russell Bernard (2012). Social Research Methods: Qualitative and Quantitative Approaches SAGE Publications


  • Predicting
  • Estimating
  • Prediction
  • Forecasting Methods
  • Models
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