Complexity in Forecasting and Predictive Models

Abstract

1. Introduction Te challenge of this special issue has been to know the state of the problem related to forecasting modeling and the creation of a model to forecast the future behavior that supports decision making by supporting real-world applications. Tis issue has been highlighted by the quality of its research work on the critical importance of advanced analytical methods, such as neural networks, sof computing, evolutionary algorithms, chaotic models, cellular automata, agent-based models, and fnite mixture minimum squares (FIMIX-PLS)