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

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


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.


Time series prediction Hybrid model Evolutionary algorithms 



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


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Research Tomsk Polytechnic UniversityTomskRussia

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