• Jun Zhao
  • Wei Wang
  • Chunyang Sheng
Part of the Information Fusion and Data Science book series (IFDS)


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.


Supervised 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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jun Zhao
    • 1
  • Wei Wang
    • 1
  • Chunyang Sheng
    • 2
  1. 1.Dalian University of TechnologyDalianChina
  2. 2.Shandong University of Science and TechnologyQingdaoChina

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