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    • Long-term prediction of nonlinear time series

    Metadata Updated: April 10, 2025

    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.

    Access & Use Information

    Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

    Downloads & Resources

    Dates

    Metadata Created Date November 12, 2020
    Metadata Updated Date April 10, 2025
    Data Update Frequency irregular

    Metadata Source

    Harvested from NASA Data.json

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date November 12, 2020
    Metadata Updated Date April 10, 2025
    Publisher Dashlink
    Maintainer
    Identifier DASHLINK_170
    Data First Published 2010-09-22
    Data Last Modified 2025-03-31
    Public Access Level public
    Data Update Frequency irregular
    Bureau Code 026:00
    Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Harvest Object Id 5107405d-936e-4a3c-bf95-48d8c1fa5036
    Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
    Harvest Source Title NASA Data.json
    Homepage URL https://c3.nasa.gov/dashlink/resources/170/
    Program Code 026:029
    Source Datajson Identifier True
    Source Hash 1edf71940e64a739f3cd614c5b33cd26fb8caebb6160b7f539a002c0dab383eb
    Source Schema Version 1.1

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