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Granular Computing-Based Long-Term Prediction Intervals

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

Abstract

In industrial practice, long-term prediction for process variables is fairly significant for the process industry, which is capable of providing the guidance for equipment control, operational scheduling, and decision-making. This chapter firstly introduces the basic principles of granularity partition, and a long-term prediction model for time series and factor-based prediction are developed in this chapter. In terms of time series prediction, the unequal-length temporal granules are constructed by exploiting dynamic time warping (DTW) technique, and a granular-computing (GrC)-based hybrid collaborative fuzzy clustering (HCFC) algorithm is proposed for the mentioned factor-based prediction problem. Besides, in this chapter, the long-term prediction approach is also combined with the PIs construction in order to provide the prediction reliability in the context of long-term time series task. Similarly, the PIs construction on multi-dimension problem is also introduced by employing the structure of the HCFC algorithm. To verify the effectiveness of these approaches, this chapter provides some experimental analysis on industrial data coming from an energy data center of a steel plant.

Keywords

Long-term prediction Granular computing Temporal segments FCM Partition of unequal length DTW Sequence similarity Time warping normalization Fuzzy rules Hybrid collaborative fuzzy clustering Lagrange multiplier Coverage Specificity PIs Multi-dimension Time series prediction 

References

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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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