This chapter gives an overall introduction to this book. First, we discuss the importance of the prediction for industrial process. Then, we divide the data-driven prediction methodology discussed in this book into a number of categories. Specifically, there are three categories, i.e., data feature-based methods, time scale-based ones, and prediction reliability-based ones. Besides, considering the characteristics of prediction modeling and industrial demands, this book introduces some commonly used prediction techniques, including the time series-based methods, the factor-based methods, the prediction intervals (PIs) construction methods, and the granular-based long-term prediction methods.
KeywordsSupervised learning Data-driven Prediction Industrial process Feature Prediction reliability Time series PIs Granular computing Long-term prediction Artificial neural networks Machine learning Data mining Support vector machines Kernel functions Fuzzy modeling
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