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Hybrid approach for time series forecasting based on a penalty p-spline and evolutionary optimization
E.A. Kochegurova 1, E.Yu. Repina 1, O.B. Tsekhan 2

1 Tomsk Polytechnic University, Tomsk, Russia,
2 Yanka Kupala State University of Grodno, Grodno, Belarus

 PDF, 1225 kB

DOI: 10.18287/2412-6179-CO-667

Pages: 821-829.

Full text of article: Russian language.

Abstract:
In this work, a hybrid-forecasting model is proposed. The model includes a recursive penalty P-spline with parameters adaptation based on evolutionary optimization algorithms. In short-term forecasting, especially in real-time systems, the urgent task is to increase the forecast speed without compromising its quality. High forecasting speed has been achieved by an economical computational scheme of a recurrent P-spline with a shallow depth of prehistory. When combined with the adaptation of some parameters of the P-spline, such an approach allows you to control the forecast accuracy.

Keywords:
penalized spline, smoothing spline, digital filter, impulse infinite response (IIR filter), instrumental function, amplitude and phase-frequency response.

Citation:
Kochegurova EA, RepinaEY, Tsekhan OB. Hybrid approach for time series forecasting based on a penalty p-spline and evolutionary optimization. Computer Optics 2020; 44(5): 821-829. DOI: 10.18287/2412-6179-CO-667.

Acknowledgements:
The work was funded by the Russian Foundation for Basic Research under grant #18-07-01007.

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