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