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This article is cited in 4 scientific papers (total in 4 papers)
NUMERICAL METHODS AND DATA ANALYSIS
Hybrid approach for time series forecasting based on a penalty p-spline and evolutionary optimization
E. A. Kochegurovaa, E. Yu. Repinaa, V. Tsekhanb a Tomsk Polytechnic University, Tomsk, Russ
b Yanka Kupala State University of Grodno, Grodno, Belarus
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.
Received: 13.11.2019 Accepted: 17.02.2020
Citation:
E. A. Kochegurova, E. Yu. Repina, V. Tsekhan, “Hybrid approach for time series forecasting based on a penalty p-spline and evolutionary optimization”, Computer Optics, 44:5 (2020), 821–829
Linking options:
https://www.mathnet.ru/eng/co852 https://www.mathnet.ru/eng/co/v44/i5/p821
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Abstract page: | 116 | Full-text PDF : | 189 | References: | 29 |
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