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Adaptive Time Series Prediction Model Based on a Smoothing P-spline

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Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19) (IITI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1156))

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Abstract

One major task of modern short-term forecasting is to increase its speed without deteriorating the quality. This is especially relevant when developing real-time forecasting models. The hybrid forecasting model proposed in this paper is based on a recurrent P-spline and enables adaptation of parameters by evolutionary optimization algorithms. An important characteristic of the proposed model is the use of a shallow prehistory. Besides, the recurrent P-spline has a cost-effective computational scheme; therefore, the forecast speed of the model is high. Simultaneous adaptation of several parameters of the P-spline allows forecast accuracy control. This leads to the creation of various versions of forecasting methods and synthesizing hybrid mathematical models with different structures.

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References

  1. Yin, Y., Shang, P.: Forecasting traffic time series with multivariate predicting method. Appl. Math. Comput. 291(1), 266–278 (2016)

    MathSciNet  MATH  Google Scholar 

  2. Zhang, K.Q., Qu, Z.X., Dong, Y.X., Lu, H.Y., Leng, W.N., Wang, J.Z., Zhang, W.Y.: Research on a combined model based on linear and nonlinear features—a case study of wind speed forecasting. Renewable Energy 130, 814–830 (2019)

    Article  Google Scholar 

  3. Sbrana, G., Silvestrini, A., Venditti, F.: Short-term inflation forecasting: the M.E.T.A. approach. Int. J. Forecast. 33, 1065–1081 (2017)

    Article  Google Scholar 

  4. Fu, T-c: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164–181 (2011)

    Article  Google Scholar 

  5. Wang, H., Zhangc, Q., Wud, J., Panf, S., Chene, Y.: Time series feature learning with labeled and unlabeled data. Pattern Recogn. 89, 55–66 (2019)

    Article  Google Scholar 

  6. Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control, 5th edn. Wiley, Hoboken (2015)

    MATH  Google Scholar 

  7. Montgomery, D.C., Jennings, C.L., Kulahci, M.: Introduction to Time Series Analysis and Forecasting. Wiley, Hoboken (2015)

    MATH  Google Scholar 

  8. Parmezan, A., Lee, H., Wu, F.: Metalearning for choosing feature selection algorithms in data mining: proposal of a new framework. Expert Syst. Appl. 75, 1–24 (2017)

    Article  Google Scholar 

  9. Yang, J.M.: Power system short-term load forecasting. Ph.D. thesis. Elektrotechnik und Informationstechnik der Technischen Universität, Germany, Darmstadt (2006)

    Google Scholar 

  10. Parmezan, A., Souza, V., Batistaa, G.: Evaluation of statistical and machine learning models for time series prediction: identifying the state-of-the-art and the best conditions for the use of each model. Inf. Sci. 484, 302–337 (2019)

    Article  Google Scholar 

  11. Ta, X., Wei, Y.: Research on a dissolved oxygen prediction method for recirculating aquaculture systems based on a convolution neural network. Comput. Electron. Agric. 145, 302–310 (2018)

    Article  Google Scholar 

  12. Utkin, L.V.: An imprecise extension of SVM-based machine learning models. Neurocomputing 331, 18–32 (2019)

    Article  Google Scholar 

  13. Vapnik, V.N.: The Nature of Statistical Learning Theory. Information Science and Statistics, 2nd edn. Springer, New York (1999)

    Google Scholar 

  14. Lu, C.: Wavelet fuzzy neural networks for identification and predictive control of dynamic systems. IEEE Trans. Industr. Electron. 58(7), 3046–3058 (2011)

    Article  Google Scholar 

  15. Zhang, M.L., Zhou, Z.H.: A k-nearest neighbor based algorithm for multi-label classification. In: 1st IEEE International Conference on Granular Computing, pp. 718–721. IEEE, New York (2005)

    Google Scholar 

  16. Chernoff, K., Nielsen, M.: Weighting of the k-Nearest-Neighbors. In: 20th IEEE International Conference on Pattern Recognition (ICPR), Istanbul, Turkey, pp. 666–669. IEEE (2010)

    Google Scholar 

  17. Liu, H., Zhang, S.: Noisy data elimination using mutual k-nearest neighbor for classification mining. J. Syst. Softw. 85(5), 1067–1074 (2012)

    Article  Google Scholar 

  18. de Boor, C.A.: Practical Guide to Splines. Springer, New York (2001)

    MATH  Google Scholar 

  19. Tharmaratnam, K., Claeskens, G., Croux, C., Salibián-Barrera, M.: S-estimation for penalized regression splines. J. Comput. Graph. Stat. 9(3), 609–625 (2010)

    Article  MathSciNet  Google Scholar 

  20. Budakçı, G., Dişibüyük, Ç., Goldman, R., Oruç, H.: Extending fundamental formulas from classical B-splines to quantum B-splines. J. Comput. Appl. Math. 282, 17–33 (2015)

    Article  MathSciNet  Google Scholar 

  21. Eilers, P.H.C., Marx, B.D.: Splines, knots, and penalties. Comput. Statistics 2(6), 637–653 (2010)

    Article  Google Scholar 

  22. Aydin, D., Memmedli, M.: Optimum smoothing parameter selection for penalized least squares in form of linear mixed effect models. Optimization 61(4), 459–476 (2012)

    Article  MathSciNet  Google Scholar 

  23. Kochegurova, E.A., Gorokhova, E.S.: Current estimation of the derivative of a non-stationary process based on a recurrent smoothing spline. Optoelectron. Instrum. Data Process. 52(3), 280–285 (2016)

    Article  Google Scholar 

  24. Kochegurova, E.A., Kochegurov, A.I., Rozhkova, N.E.: Frequency analysis of recurrence variational P-splines. Optoelectron. Instrum. Data Process. 53(6), 591–598 (2017)

    Article  Google Scholar 

  25. Martín, A., Lara-Cabrera, R., Fuentes-Hurtado, F., Naranjo, V., Camacho, D.: EvoDeep: a new evolutionary approach for automatic Deep Neural Networks parametrization. J. Parallel Distrib. Comput. 117, 180–191 (2018)

    Article  Google Scholar 

  26. Yang, X.S.: Nature-Inspired Optimization Algorithms, 1st edn. Elsevier, Amsterdam (2014)

    MATH  Google Scholar 

  27. Khashei, M., Bijari, M.: A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Appl. Soft Comput. 11(2), 2664–2675 (2011)

    Article  Google Scholar 

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Acknowledgment

The reported study was funded by RFBR according to the research project № 18-07-01007.

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Authors and Affiliations

  1. National Research Tomsk Polytechnic University, Lenina Avenue, 30, 634050, Tomsk, Russia

    Elena Kochegurova, Ivan Khozhaev & Elizaveta Repina

Authors
  1. Elena Kochegurova
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  2. Ivan Khozhaev
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  3. Elizaveta Repina
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Corresponding author

Correspondence to Elena Kochegurova .

Editor information

Editors and Affiliations

  1. Rostovskogo Strelkovogo Polka Narodnogo, Rostov State Transport University, Rostov-on-Don, Russia

    Sergey Kovalev

  2. Bauman Moscow State Technical University, Moscow, Russia

    Valery Tarassov

  3. Department of Computer Science, VSB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Vaclav Snasel

  4. Rostovskogo Strelkovogo Polka, Rostov State Transport University, Rostov-on-Don, Russia

    Andrey Sukhanov

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Kochegurova, E., Khozhaev, I., Repina, E. (2020). Adaptive Time Series Prediction Model Based on a Smoothing P-spline. In: Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19). IITI 2019. Advances in Intelligent Systems and Computing, vol 1156. Springer, Cham. https://doi.org/10.1007/978-3-030-50097-9_45

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