Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Operations Research and Decision Support

Decisions are made for the future, Futures Studies investigate possible fan of futures scenarios and its probabilities for easier decision making processes, taking into consideration unprecedented future events (wildcards) for clear vision of the future. Markov Chain method generally used as a mean of characterizing or summarizing data and of projecting the time path of certain variables. The main ideas of this paper rely on using the slope and curvature of variables historical data time series to convert it to historical states time series, followed by Association Rule Mining to build a Markov Chain Transition Probability Matrix from the variables historical states time series. Finally we introduce hybridization between the Futures Studies and Markov chain by building "a Dynamic Multiplicative Model for Probability Impact Analysis". Our introduced methodologies allow studying the occurrence impact of multiple wildcards on multiple variables states' probabilities at future years

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