A Comparison between Deep Belief Network and LSTM in Chaotic Time Series Forecasting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10249628" target="_blank" >RIV/61989100:27240/21:10249628 - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/doi/pdf/10.1145/3490725.3490749" target="_blank" >https://dl.acm.org/doi/pdf/10.1145/3490725.3490749</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3490725.3490749" target="_blank" >10.1145/3490725.3490749</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Comparison between Deep Belief Network and LSTM in Chaotic Time Series Forecasting

Popis výsledku v původním jazyce

The application of deep neural networks in forecasting time series data is increasingly popular, aiming to improve prediction accuracy in this problem. However, as for chaotic time series, a special kind of time series data generated from the deterministic dynamics of a nonlinear system, there are very few research works applying deep neural networks to forecast this kind of time series data. So far, Deep Belief Network (DBN) and Long Short Term Memory (LSTM) are two kinds of deep neural networks used to extract meaningful features from the chaotic time series before forecasting. This article aims to compare the prediction performance of the LSTM model with that of the DBN model on chaotic time series data. Experimental results on six synthetic and real-world datasets in this study show that LSTM brings out better prediction accuracy than DBN in terms of three evaluation criteria. (C) 2021 ACM.

Název v anglickém jazyce

A Comparison between Deep Belief Network and LSTM in Chaotic Time Series Forecasting

Popis výsledku anglicky

The application of deep neural networks in forecasting time series data is increasingly popular, aiming to improve prediction accuracy in this problem. However, as for chaotic time series, a special kind of time series data generated from the deterministic dynamics of a nonlinear system, there are very few research works applying deep neural networks to forecast this kind of time series data. So far, Deep Belief Network (DBN) and Long Short Term Memory (LSTM) are two kinds of deep neural networks used to extract meaningful features from the chaotic time series before forecasting. This article aims to compare the prediction performance of the LSTM model with that of the DBN model on chaotic time series data. Experimental results on six synthetic and real-world datasets in this study show that LSTM brings out better prediction accuracy than DBN in terms of three evaluation criteria. (C) 2021 ACM.

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

ACM International Conference Proceeding Series 2021

ISBN

978-1-4503-8424-7

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

157-163

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Chang-čou

Datum konání akce

17. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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