Hybrid approach Wavelet seasonal autoregressive integrated moving averagemodel (WSARIMA) for modeling time series

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F21%3APU140167" target="_blank" >RIV/00216305:26510/21:PU140167 - isvavai.cz</a>

Výsledek na webu

<a href="https://aip.scitation.org/doi/pdf/10.1063/5.0041734" target="_blank" >https://aip.scitation.org/doi/pdf/10.1063/5.0041734</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/5.0041734" target="_blank" >10.1063/5.0041734</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hybrid approach Wavelet seasonal autoregressive integrated moving averagemodel (WSARIMA) for modeling time series

Popis výsledku v původním jazyce

Many prognosis studies have been conducted for a long time. There are many established and widely accepted prediction methods, such as linear extrapolation and SARIMA. However, their performance is far from perfect, especially when the time series is highly volatile. In this paper, we propose a hybrid prediction scheme that combines the classical SARIMA method and the wavelet transform (WT). Wavelet transform (WT) has emerged as an effective tool in decomposing time series into different components, which allows for improved prediction accuracy. However, this issue has so far been insufficiently tested and tried to predict different time series. Our goal is therefore to integrate modeling approaches as a decision support tool. The results of an empirical study show that this method can achieve high accuracy in prediction. Based on the results of the created model, it can be stated that the hybrid WSARIMA model overperformed the SARIMA model.

Název v anglickém jazyce

Hybrid approach Wavelet seasonal autoregressive integrated moving averagemodel (WSARIMA) for modeling time series

Popis výsledku anglicky

Many prognosis studies have been conducted for a long time. There are many established and widely accepted prediction methods, such as linear extrapolation and SARIMA. However, their performance is far from perfect, especially when the time series is highly volatile. In this paper, we propose a hybrid prediction scheme that combines the classical SARIMA method and the wavelet transform (WT). Wavelet transform (WT) has emerged as an effective tool in decomposing time series into different components, which allows for improved prediction accuracy. However, this issue has so far been insufficiently tested and tried to predict different time series. Our goal is therefore to integrate modeling approaches as a decision support tool. The results of an empirical study show that this method can achieve high accuracy in prediction. Based on the results of the created model, it can be stated that the hybrid WSARIMA model overperformed the SARIMA model.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

AIP Conference Proceedings

ISBN

978-0-7354-4077-7

ISSN

—

e-ISSN

—

Počet stran výsledku

10

Strana od-do

„090001-1“-„090001-10“

Název nakladatele

AIP Publishing

Místo vydání

neuveden

Místo konání akce

Sofia

Datum konání akce

7. 6. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000664205600026