Long-term prediction of nonlinear time series
This paper is about applying recurrent least squares support vector machines (LS-SVM) on three ESTSP08 competition datasets. Least squares
support vector machines are used as nonlinear models in order to avoid local
minima problems. Then prediction task is re-formulated as function approximation
task. Recurrent LS-SVM uses nonlinear autoregressive exogenous (NARX) model
to build nonlinear regressor, by estimating in each iteration the next output value,
given the past output and input measurements.
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode |
[
"026:00"
]
|
| contactPoint |
{
"fn": "Indir Jaganjac",
"@type": "vcard:Contact",
"hasEmail": "mailto:ijaganjac@yahoo.com"
}
|
| description | This paper is about applying recurrent least squares support vector machines (LS-SVM) on three ESTSP08 competition datasets. Least squares support vector machines are used as nonlinear models in order to avoid local minima problems. Then prediction task is re-formulated as function approximation task. Recurrent LS-SVM uses nonlinear autoregressive exogenous (NARX) model to build nonlinear regressor, by estimating in each iteration the next output value, given the past output and input measurements. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "I._Jaganjac_ESTSP08.pdf",
"format": "PDF",
"mediaType": "application/pdf",
"description": "ESTSP08",
"downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/I._Jaganjac_ESTSP08.pdf"
}
]
|
| identifier | DASHLINK_170 |
| issued | 2010-09-22 |
| keyword |
[
"ames",
"dashlink",
"nasa"
]
|
| landingPage | https://c3.nasa.gov/dashlink/resources/170/ |
| modified | 2025-03-31 |
| programCode |
[
"026:029"
]
|
| publisher |
{
"name": "Dashlink",
"@type": "org:Organization"
}
|
| title | Long-term prediction of nonlinear time series |
