Sensitivity analysis of echo state networks for forecasting pseudo-periodic time series

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86099103" target="_blank" >RIV/61989100:27240/15:86099103 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/15:86099103

Výsledek na webu

<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7492768" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7492768</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SOCPAR.2015.7492768" target="_blank" >10.1109/SOCPAR.2015.7492768</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sensitivity analysis of echo state networks for forecasting pseudo-periodic time series

Popis výsledku v původním jazyce

This paper presents an analysis of the impact of the parameters of an Echo State Network (ESN) on its performance. In particular, we are interested on the parameter behaviour when the model is used for forecasting pseudo-periodic time series. According previous literature, the spectral radius of the hidden-hidden weight matrix of the ESN is a relevant parameter on the model performance. It impacts in the memory capacity and in the accuracy the model. Small values of the spectral radius are recommended for modelling time-series that require short fading memory. On the other hand, a matrix with spectral radius close to the unity is recommended for processing long memory time series. In this article, we figure out that the periodicity of the data is also an important factor to consider in the design of the ESN. Our results show that the better forecasting (according to two metrics of performance) occurs when the hidden-hidden weight matrix has spectral value equal to 0.5. For our analysis we use a public synthetic dataset that has a high periodicity. (C) 2015 IEEE.

Název v anglickém jazyce

Sensitivity analysis of echo state networks for forecasting pseudo-periodic time series

Popis výsledku anglicky

This paper presents an analysis of the impact of the parameters of an Echo State Network (ESN) on its performance. In particular, we are interested on the parameter behaviour when the model is used for forecasting pseudo-periodic time series. According previous literature, the spectral radius of the hidden-hidden weight matrix of the ESN is a relevant parameter on the model performance. It impacts in the memory capacity and in the accuracy the model. Small values of the spectral radius are recommended for modelling time-series that require short fading memory. On the other hand, a matrix with spectral radius close to the unity is recommended for processing long memory time series. In this article, we figure out that the periodicity of the data is also an important factor to consider in the design of the ESN. Our results show that the better forecasting (according to two metrics of performance) occurs when the hidden-hidden weight matrix has spectral value equal to 0.5. For our analysis we use a public synthetic dataset that has a high periodicity. (C) 2015 IEEE.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 2015 seventh International conference on soft computing and pattern recognition, SOCPAR 2015

ISBN

978-1-4673-9360-7

ISSN

2381-7542

e-ISSN

—

Počet stran výsledku

6

Strana od-do

328-333

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Fukuoka

Datum konání akce

13. 11. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000383091300057