Titlebook: Data-Driven Prediction for Industrial Processes and Their Applications; Jun Zhao,Wei Wang,Chunyang Sheng Book 2018 Springer International

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Colin J. Theaker,Graham R. Brookestrial 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.

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Introduction,trial 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.

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Data Preprocessing Techniques,, we supplement a varied window similarity measure method, the segmented shape-representation-based method, and the non-equal-length granules correlation method for industrial data imputation. With respect to the high level noise embodied in raw data, we then give an introduction to the well-known e

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Industrial Time Series Prediction,ine (SVM) model, are also presented in this chapter. Specifically, an improved GP-based ESN model is proposed for time series prediction, in which the output weights in ESN modeled by using GP avoids the ill-conditioned phenomenon associated with the generic ESN version. A number of case studies rel

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