Sensitivity analysis of echo state networks for forecasting pseudo-periodic time series
The result's identifiers
Result code in 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>
Alternative codes found
RIV/61989100:27740/15:86099103
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Sensitivity analysis of echo state networks for forecasting pseudo-periodic time series
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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
—
Number of pages
6
Pages from-to
328-333
Publisher name
IEEE
Place of publication
New York
Event location
Fukuoka
Event date
Nov 13, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000383091300057