This article is cited 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. Kochegurova^a, E. Yu. Repina^a, V. Tsekhan<sup>b

a</sup> 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

DOI: 10.18287/2412-6179-CO-667