Abstract
Since the global recession of 2008–2009, it has been much more widely understood that reliable economic forecasting is needed in business decision-making. Of special interest are the forecasting methods based on explanatory variables (economic drivers), the most popular of which is the Auto-Regressive Integrated Moving-Average with eXplanatory variables (ARIMAX) model. A limitation of this approach, however, is the assumption of linear relationships between the explanatory variables and the target variable. Genetic programming is a potential solution for representing nonlinearity and a hybrid scheme of integrating static and dynamic nonlinear transforms into the ARIMAX models is proposed in the chapter. From an implementation point of the view the proposed solution has several advantages, such as: optimal synergy between two well-known approaches like GP and ARIMAX, avoiding the need for developing a solid theoretical alternative for nonlinear time series modeling, using available forecasting software, and low efforts to train the final user. The proposed approach is illustrated with two examples from real business applications in the area of raw materials forecasting.
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Notes
- 1.
The forecasting time horizon classification is according to Makridakis et al. (1998).
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Kordon, A.K. (2014). Applying Genetic Programming in Business Forecasting. In: Riolo, R., Moore, J., Kotanchek, M. (eds) Genetic Programming Theory and Practice XI. Genetic and Evolutionary Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0375-7_6
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