engleski

Widely Applicable Multi-Variate Decision Support Model for Market Trend Analysis and Prediction with Case Study in Retail

Chapter explains efficient ways of dealing with business problems of analyzing market environment and market trends under complex circumstances using heterogeneous data source. Under the assumption that used data can be expressed as time series, widely applicable multi variate model is explained together with case study in textile retail. This Chapter includes an overview of research conducted with a brief explanation of approaches and models available today. A widely applicable multi- variate decision support model is presented with advantages, limitations, and several variations for development. The explanation is based on textile retail case study with model wide range of possible applications in perspective. Complex business environment issues are simulated with explanation of several important global trends in textile retail in past seasons. Non-traditional approaches are revised as tools for a better understanding of modern market trends as well as references in relevant literature. A widely applicable multi-variate decision support model and its usage is presented through built stages and simulated. Model concept is based on specific time series transformation method in combination with Bayesian logic and Bayesian network as final business logic layer with front end interface built with open source Bayesian network tool. Explained case study provides one of the most challenging issue in textile retail: market trends seasonal/weather dependence. Separate outcomes for different scenario analysis approaches are presented on real life data from a textile retail chain located in Zagreb, Croatia. Chapter ends with a discussion about similar research’s, wide applicability of presented model with references for future research.

Bayesian Network ; Bayesian Logic ; Directed Acyclic Graph ; Annealed MAP (AMAP)

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