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 Forecasting

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).
References:
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

 Formal Modelling

Formal modelling means representing a system by a formal model. Formal modelling is well defined and described as the application of a fairly broad variety of theoretical computer science fundamentals, in particular logic calculi, formal languages, automata theory, and program semantics, but also type systems and algebraic data types to problems in software and hardware specification and verification (Monin, 2003).
A wide variety of formal models have been created for supporting policy-making approach. The work of Boer et al. (2011) discusses how the interests and field theory promoted by public administration as a stakeholder in policy argumentation directly arise from its problem solving activities. They propose a framework for public administration, based on a model for diagnosis problem solving.
Policy-makers are usually facing the problem of finding the relevant parts of the regulations with their impacts in order to change the legal texts consistently and coherently. As a solution to these demands, Szoke et al. (2011) presented a semantic enrichment formal approach for policy makers which allows to reason on ambiguousness of legal texts and infer new knowledge. Wyner et al. (2011) proposed a semantic based model: they discussed the role of semantic models and ontologies in modelling policy-making, by describing policy making such as a cyclical, multi-stage process, with several stages: evaluation, agenda setting, policy formulation, decision, implementation.
Related term: Formal Model, Formal Method
References:
Monin, J.F. (2003), Understanding Formal Methods, Springer, 2003, XVI, 276.
Boer, A., Van Engers T., and Sileno G.(2011), A Problem Solving Model for Regulatory Policy Making. In Proceedings of the Workshop on Modelling Policy-making (MPM 2011), in conjunction with The 24th International Conference on Legal Knowledge and Information Systems (JURIX 2011). Vienna (Austria), 2011.
Szoke, A., Forhecz, A., Mascar, K., Strausz, G. (2011), Linking Semantic Enrichment to Legal Documents. In Proceedings of the Workshop on Modelling Policy-making (MPM 2011), Vienna (Austria), 2011.
Wyner, A. Atkinson, K and Bench-Capon ,T. (2011), Semantic Models and Ontologies in Modelling Policy-making. In Proceedings of the Workshop on Modelling Policy-making (MPM 2011), Vienna (Austria), 2011.