Time series prediction using support vector machines, the orthogonal and the regularized orthogonal least-squares algorithms

DOI10.1080/0020772021000017317zbMath1013.93055OpenAlexW2085900457MaRDI QIDQ4795906

No author found.

Publication date: 19 June 2003

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/0020772021000017317

zbMATH Keywords

predictionsupport vector machinesorthogonal least squares algorithmregularized orthogonal least squares algorithm

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Least squares and related methods for stochastic control systems (93E24)

Related Items

Modelling of river discharges and rainfall using radial basis function networks based on support vector regression ⋮ Model selection approaches for non-linear system identification: a review ⋮ Linear programming support vector regression with wavelet kernel: A new approach to nonlinear dynamical systems identification ⋮ A time-sequence-based fuzzy support vector machine adaptive filter for tremor cancelling for microsurgery ⋮ Iterative learning identification for a class of parabolic distributed parameter systems

Uses Software

• LOQO
• Mosek

Cites Work

• Unnamed Item
• Unnamed Item
• Nonlinear prediction of chaotic time series
• 10.1162/15324430260185637
• Modelling and analysis of non-linear time series
• Orthogonal parameter estimation algorithm for non-linear stochastic systems
• Orthogonal least squares methods and their application to non-linear system identification
• Algorithms for minimal model structure detection in nonlinear dynamic system identification
• Scale-sensitive dimensions, uniform convergence, and learnability
• LOQO:an interior point code for quadratic programming
• Regularized orthogonal least squares algorithm for constructing radial basis function networks