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2018 | OriginalPaper | Buchkapitel

3. Industrial Time Series Prediction

verfasst von : Jun Zhao, Wei Wang, Chunyang Sheng

Erschienen in: Data-Driven Prediction for Industrial Processes and Their Applications

Verlag: Springer International Publishing

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Abstract

Time series prediction is a significant way for forecasting the variables involved in industrial process, which usually identifies the latent rules hidden behind the time series data of the variables by means of auto-regression. In this chapter we introduce the phase space reconstruction technique, which aims to construct the training dataset for modeling, and then a series of data-driven machine learning methods are provided for time series prediction, where some well-known artificial neural networks (ANNs) models are introduced, and a dual estimation-based echo state network (ESN) model is particularly proposed to simultaneously estimate the uncertainties of the output weights and the internal states by using a nonlinear Kalman-filter and a linear one for noisy industrial time series. In addition, the kernel based methods, including Gaussian processes (GP) model and support vector machine (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 related to industrial energy system are provided to validate the performance of these methods.

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Literatur
1.
Zurück zum Zitat Takens, F. (1981). Detecting strange attractors in turbulence. Lecture Notes in Math, 898, 361–381.MathSciNetMATH Takens, F. (1981). Detecting strange attractors in turbulence. Lecture Notes in Math, 898, 361–381.MathSciNetMATH
2.
Zurück zum Zitat Kennel, M. B., Brown, R., & Abarbanel, H. D. (1992). Determining embedding dimension for phase-space reconstruction using a geometrical construction. Physical Review A Atomic Molecular & Optical Physics, 45(6), 3403–3411.CrossRef Kennel, M. B., Brown, R., & Abarbanel, H. D. (1992). Determining embedding dimension for phase-space reconstruction using a geometrical construction. Physical Review A Atomic Molecular & Optical Physics, 45(6), 3403–3411.CrossRef
3.
Zurück zum Zitat Cao, L. (1997). Practical method for determining the minimum embedding dimension of a scalar time series. Physica D-Nonlinear Phenomena, 110(1-2), 43–50.CrossRef Cao, L. (1997). Practical method for determining the minimum embedding dimension of a scalar time series. Physica D-Nonlinear Phenomena, 110(1-2), 43–50.CrossRef
4.
Zurück zum Zitat Fraser, A. M., & Swinney, H. L. (1986). Independent coordinates for strange attractors from mutual information. Physical Review A General Physics, 33(2), 1134.MathSciNetCrossRef Fraser, A. M., & Swinney, H. L. (1986). Independent coordinates for strange attractors from mutual information. Physical Review A General Physics, 33(2), 1134.MathSciNetCrossRef
5.
Zurück zum Zitat Kim, H. S., Eykholt, R., & Salas, J. D. (1999). Nonlinear dynamics, delay times, and embedding windows. Physica D Nonlinear Phenomena, 127(1–2), 48–60.CrossRef Kim, H. S., Eykholt, R., & Salas, J. D. (1999). Nonlinear dynamics, delay times, and embedding windows. Physica D Nonlinear Phenomena, 127(1–2), 48–60.CrossRef
6.
Zurück zum Zitat Brock, W. A., Hsieh, D. A., & LeBaron, B. (1991). Nonlinear dynamics, chaos, and instability: Statistical theory and economic evidence. Cambridge: MIT Press. Brock, W. A., Hsieh, D. A., & LeBaron, B. (1991). Nonlinear dynamics, chaos, and instability: Statistical theory and economic evidence. Cambridge: MIT Press.
7.
Zurück zum Zitat Han, M., & Xu, M. (2018). Laplacian echo state network for multivariate time series prediction. IEEE Transactions on Neural Networks and Learning System, 29(1), 238–244.MathSciNetCrossRef Han, M., & Xu, M. (2018). Laplacian echo state network for multivariate time series prediction. IEEE Transactions on Neural Networks and Learning System, 29(1), 238–244.MathSciNetCrossRef
8.
Zurück zum Zitat Bishop, C. M. (2006). Pattern recognition and machine learning (Information Science and Statistics). New York: Springer.MATH Bishop, C. M. (2006). Pattern recognition and machine learning (Information Science and Statistics). New York: Springer.MATH
9.
Zurück zum Zitat Zhao, J., Liu, Q., Wang, W., et al. (2012). Hybrid neural prediction and optimized adjustment for coke oven gas system in steel industry. IEEE Transactions on Neural Networks and Learning Systems, 23(3), 439–450.CrossRef Zhao, J., Liu, Q., Wang, W., et al. (2012). Hybrid neural prediction and optimized adjustment for coke oven gas system in steel industry. IEEE Transactions on Neural Networks and Learning Systems, 23(3), 439–450.CrossRef
10.
Zurück zum Zitat An, S., Liu, W., & Venkatesh, S. (2007). Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression. Pattern Recognition, 40(8), 2154–2162.CrossRef An, S., Liu, W., & Venkatesh, S. (2007). Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression. Pattern Recognition, 40(8), 2154–2162.CrossRef
11.
Zurück zum Zitat Hong, W. G., Feng, Q., Yan, C. L., Wen, L. D., & Lu, W. (2008). Identification and control nonlinear systems by a dissimilation particle swarm optimization-based Elman neural network. Nonlinear Analysis Real World Applications, 9, 1345–1360.MathSciNetCrossRef Hong, W. G., Feng, Q., Yan, C. L., Wen, L. D., & Lu, W. (2008). Identification and control nonlinear systems by a dissimilation particle swarm optimization-based Elman neural network. Nonlinear Analysis Real World Applications, 9, 1345–1360.MathSciNetCrossRef
12.
Zurück zum Zitat Li, X., Chen, Z. Q., & Yuan, Z. Z. (2000). Nonlinear stable adaptive control based upon Elman networks. Applied Mathematics–A Journal of Chinese Universities, Series, B15, 332–340.MathSciNetMATH Li, X., Chen, Z. Q., & Yuan, Z. Z. (2000). Nonlinear stable adaptive control based upon Elman networks. Applied Mathematics–A Journal of Chinese Universities, Series, B15, 332–340.MathSciNetMATH
13.
Zurück zum Zitat Liou, C. Y., Huang, J. C., & Yang, W. C. (2008). Modeling word perception using the Elman network. Neurocomputing, 71, 3150–3157.CrossRef Liou, C. Y., Huang, J. C., & Yang, W. C. (2008). Modeling word perception using the Elman network. Neurocomputing, 71, 3150–3157.CrossRef
14.
Zurück zum Zitat Köker, R. (2005). Reliability-based approach to the inverse kinematics solution of robots using Elman’s networks. Engineering Applications of Artificial Intelligence, 18(6), 685–693.CrossRef Köker, R. (2005). Reliability-based approach to the inverse kinematics solution of robots using Elman’s networks. Engineering Applications of Artificial Intelligence, 18(6), 685–693.CrossRef
15.
Zurück zum Zitat Welch, G., & Bishop, G. (1995). An introduction to the Kalman filter, Technical Report TR 95-041. University of North Carolina, Department of Computer Science. Welch, G., & Bishop, G. (1995). An introduction to the Kalman filter, Technical Report TR 95-041. University of North Carolina, Department of Computer Science.
16.
Zurück zum Zitat Jaeger, H. (2002). Tutorial on training recurrent neural networks, covering BPTT, RTRL, EKF and “Echo State Network” approach, Technical Report GMD Report 159. German National Research Center for Information Technology. Jaeger, H. (2002). Tutorial on training recurrent neural networks, covering BPTT, RTRL, EKF and “Echo State Network” approach, Technical Report GMD Report 159. German National Research Center for Information Technology.
17.
Zurück zum Zitat Farkaš, I., Bosák, R., & Gergeľ, P. (2016). Computational analysis of memory capacity in echo state networks. Neural Networks, 83, 109–120.CrossRef Farkaš, I., Bosák, R., & Gergeľ, P. (2016). Computational analysis of memory capacity in echo state networks. Neural Networks, 83, 109–120.CrossRef
18.
Zurück zum Zitat Jaeger, H., & Haas, H. (2004). Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science, 304(5667), 78–80.CrossRef Jaeger, H., & Haas, H. (2004). Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science, 304(5667), 78–80.CrossRef
19.
Zurück zum Zitat Jaeger, H. (2005). Reservoir riddles: Suggestions for echo state network research (pp. 1460–1462). In Proceedings of the International Joint Conference on Neural Networks. Jaeger, H. (2005). Reservoir riddles: Suggestions for echo state network research (pp. 1460–1462). In Proceedings of the International Joint Conference on Neural Networks.
20.
Zurück zum Zitat Shi, Z. W., & Han, M. (2007). Support vector echo-state machine for chaotic time-series prediction. IEEE Transactions on Neural Network, 18(2), 359–372.CrossRef Shi, Z. W., & Han, M. (2007). Support vector echo-state machine for chaotic time-series prediction. IEEE Transactions on Neural Network, 18(2), 359–372.CrossRef
21.
Zurück zum Zitat Liu, Y., Zhao, J., & Wang, W. (2009). Improved echo state network based on data-driven and its application in prediction of blast furnace gas output. Acta Automatica Sinica., 35, 731–738.CrossRef Liu, Y., Zhao, J., & Wang, W. (2009). Improved echo state network based on data-driven and its application in prediction of blast furnace gas output. Acta Automatica Sinica., 35, 731–738.CrossRef
22.
Zurück zum Zitat Golub, G. H., & van Loan, C. F. (1983). Matrix computations. Baltimore: The Johns Hopkins University Press.MATH Golub, G. H., & van Loan, C. F. (1983). Matrix computations. Baltimore: The Johns Hopkins University Press.MATH
23.
Zurück zum Zitat Saxén, H., & Pettersson, F. (2005). A simple method for selection of inputs and structure of feedforward neural networks. Computers & Chemical Engineering, 30(6), 1038–1045. Saxén, H., & Pettersson, F. (2005). A simple method for selection of inputs and structure of feedforward neural networks. Computers & Chemical Engineering, 30(6), 1038–1045.
24.
Zurück zum Zitat Saxen, H., & Pettersson, F. (2007). Nonlinear prediction of the hot metal silicon content in the blast furnace. Transactions of the Iron & Steel Institute of Japan, 47(12), 1732–1737.CrossRef Saxen, H., & Pettersson, F. (2007). Nonlinear prediction of the hot metal silicon content in the blast furnace. Transactions of the Iron & Steel Institute of Japan, 47(12), 1732–1737.CrossRef
25.
Zurück zum Zitat Wan, E. A., & Nelson, A. T. (2001). Dual extended Kalman filter methods. In S. Haykin (Ed.), Kalman filtering and neural networks (pp. 123–174). Chichester: Wiley.CrossRef Wan, E. A., & Nelson, A. T. (2001). Dual extended Kalman filter methods. In S. Haykin (Ed.), Kalman filtering and neural networks (pp. 123–174). Chichester: Wiley.CrossRef
26.
Zurück zum Zitat Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer.CrossRef Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer.CrossRef
27.
Zurück zum Zitat Bennett, K. P., & Mangasarian, O. L. (1992). Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software, 1, 23–34.CrossRef Bennett, K. P., & Mangasarian, O. L. (1992). Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software, 1, 23–34.CrossRef
28.
Zurück zum Zitat Cortes, C., & Vapnik, V. (1995). Support vector networks. Machine Learning, 20, 273–297.MATH Cortes, C., & Vapnik, V. (1995). Support vector networks. Machine Learning, 20, 273–297.MATH
29.
Zurück zum Zitat Fletcher, R. (1989). Practical methods of optimization. New York: Wiley.MATH Fletcher, R. (1989). Practical methods of optimization. New York: Wiley.MATH
30.
Zurück zum Zitat Schölkopf, B., & Smola, A. J. (2002). Learning with kernels. Cambridge: MIT Press.MATH Schölkopf, B., & Smola, A. J. (2002). Learning with kernels. Cambridge: MIT Press.MATH
31.
Zurück zum Zitat Gestel, V., Suykens, J. A. K., et al. (2001). Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Transactions on Neural Networks, 12(4), 809–821.CrossRef Gestel, V., Suykens, J. A. K., et al. (2001). Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Transactions on Neural Networks, 12(4), 809–821.CrossRef
32.
Zurück zum Zitat Suykens, J., & Vandewalle, J. (1999). Least squares support vector machines classifiers. Neural Processing Letters, 9(3), 293–300.CrossRef Suykens, J., & Vandewalle, J. (1999). Least squares support vector machines classifiers. Neural Processing Letters, 9(3), 293–300.CrossRef
33.
Zurück zum Zitat Diykh, M., Li, Y., & Wen, P. (2017). Classify epileptic EEG signals using weighted complex networks based community structure detection. Expert Systems with Applications, 90(30), 87–100.CrossRef Diykh, M., Li, Y., & Wen, P. (2017). Classify epileptic EEG signals using weighted complex networks based community structure detection. Expert Systems with Applications, 90(30), 87–100.CrossRef
34.
Zurück zum Zitat Zhao, J., Wang, W., Pedrycz, W., et al. (2012). Online parameter optimization-based prediction for converter gas system by parallel strategies. IEEE Transactions on Control Systems Technology, 20(3), 835–845.CrossRef Zhao, J., Wang, W., Pedrycz, W., et al. (2012). Online parameter optimization-based prediction for converter gas system by parallel strategies. IEEE Transactions on Control Systems Technology, 20(3), 835–845.CrossRef
Metadaten
Titel
Industrial Time Series Prediction
verfasst von
Jun Zhao
Wei Wang
Chunyang Sheng
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-94051-9_3