Advertisement

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

  • Elena KochegurovaEmail author
  • Ivan Khozhaev
  • Elizaveta Repina
Conference paper
  • 8 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1156)

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.

Keywords

Time series prediction Hybrid model Evolutionary algorithms 

Notes

Acknowledgment

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

References

  1. 1.
    Yin, Y., Shang, P.: Forecasting traffic time series with multivariate predicting method. Appl. Math. Comput. 291(1), 266–278 (2016)MathSciNetzbMATHGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 3.
    Sbrana, G., Silvestrini, A., Venditti, F.: Short-term inflation forecasting: the M.E.T.A. approach. Int. J. Forecast. 33, 1065–1081 (2017)CrossRefGoogle Scholar
  4. 4.
    Fu, T-c: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164–181 (2011)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 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)zbMATHGoogle Scholar
  7. 7.
    Montgomery, D.C., Jennings, C.L., Kulahci, M.: Introduction to Time Series Analysis and Forecasting. Wiley, Hoboken (2015)zbMATHGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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. 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)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 12.
    Utkin, L.V.: An imprecise extension of SVM-based machine learning models. Neurocomputing 331, 18–32 (2019)CrossRefGoogle Scholar
  13. 13.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Information Science and Statistics, 2nd edn. Springer, New York (1999)Google Scholar
  14. 14.
    Lu, C.: Wavelet fuzzy neural networks for identification and predictive control of dynamic systems. IEEE Trans. Industr. Electron. 58(7), 3046–3058 (2011)CrossRefGoogle Scholar
  15. 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. 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. 17.
    Liu, H., Zhang, S.: Noisy data elimination using mutual k-nearest neighbor for classification mining. J. Syst. Softw. 85(5), 1067–1074 (2012)CrossRefGoogle Scholar
  18. 18.
    de Boor, C.A.: Practical Guide to Splines. Springer, New York (2001)zbMATHGoogle Scholar
  19. 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)MathSciNetCrossRefGoogle Scholar
  20. 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)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Eilers, P.H.C., Marx, B.D.: Splines, knots, and penalties. Comput. Statistics 2(6), 637–653 (2010)CrossRefGoogle Scholar
  22. 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)MathSciNetCrossRefGoogle Scholar
  23. 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)CrossRefGoogle Scholar
  24. 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)CrossRefGoogle Scholar
  25. 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)CrossRefGoogle Scholar
  26. 26.
    Yang, X.S.: Nature-Inspired Optimization Algorithms, 1st edn. Elsevier, Amsterdam (2014)zbMATHGoogle Scholar
  27. 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)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Research Tomsk Polytechnic UniversityTomskRussia

Personalised recommendations