Adaptive Time Series Prediction Model Based on a Smoothing P-spline

Elena Kochegurova, Ivan Khozhaev, Elizaveta Repina

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationProceedings of the 4th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2019
EditorsSergey Kovalev, Andrey Sukhanov, Valery Tarassov, Vaclav Snasel
PublisherSpringer Paris
Pages445-455
Number of pages11
ISBN (Print)9783030500962
DOIs
Publication statusPublished - 2020
Event4th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2019 - Ostrava-Prague, Czech Republic
Duration: 2 Dec 20197 Dec 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1156 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference4th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2019
CountryCzech Republic
CityOstrava-Prague
Period2.12.197.12.19

Keywords

  • Evolutionary algorithms
  • Hybrid model
  • Time series prediction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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