Hybrid approach for time series forecasting based on a penalty p-spline and evolutionary optimization

Elena Alekseevna Kochegurova, Elizaveta Yuryevna Repina, Olga Borisovna Tsekhan

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)821-829
Number of pages9
JournalComputer Optics
Volume44
Issue number5
DOIs
Publication statusPublished - 1 Sep 2020

Keywords

  • Amplitude and phase-frequency response
  • Digital filter
  • Impulse infinite response (IIR filter)
  • Instrumental function
  • Penalized spline
  • Smoothing spline

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Computer Science Applications
  • Electrical and Electronic Engineering

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