{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Indir Jaganjac", "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\r\n\r\nsupport vector machines are used as nonlinear models in order to avoid local\r\n\r\nminima problems. Then prediction task is re-formulated as function approximation\r\n\r\ntask. Recurrent LS-SVM uses nonlinear autoregressive exogenous (NARX) model\r\n\r\nto build nonlinear regressor, by estimating in each iteration the next output value,\r\n\r\ngiven the past output and input measurements.", "distribution": [{"@type": "dcat:Distribution", "description": "ESTSP08", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/I._Jaganjac_ESTSP08.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "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": {"@type": "org:Organization", "name": "Dashlink"}, "title": "Long-term prediction of nonlinear time series"}