Improved Kernel Recursive Least Squares Algorithm based Online Prediction for Nonstationary Time Series

We present an improved kernel recursive least squares (KRLS) algorithm for the online prediction of nonstationary time series. In order to adaptively sparsify a selected kernel dictionary for the KRLS algorithm, the approximate linear dependency (ALD) criterion based KRLS algorithm is combined with...

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Bibliographic Details
Published in:IEEE signal processing letters Vol. 27; p. 1
Main Authors: Guo, Jinhua, Chen, Hao, Chen, Songhang
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Approximate linear dependency
Approximation algorithms
Computer simulation
Dictionaries
Dynamic characteristics
forgetting factor
Heuristic algorithms
Kernel
kernel recursive least squares
Kernels
Least squares
Mapping
Mathematical models
nonstationary time series
online prediction
Prediction algorithms
quantized kernel recursive least squares
Signal processing algorithms
Time series
Time series analysis
ISSN:1070-9908, 1558-2361
Online Access:Get full text
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Summary:We present an improved kernel recursive least squares (KRLS) algorithm for the online prediction of nonstationary time series. In order to adaptively sparsify a selected kernel dictionary for the KRLS algorithm, the approximate linear dependency (ALD) criterion based KRLS algorithm is combined with the quantized kernel recursive least squares algorithm to provide an initial framework. In order to sufficiently track the strongly changeable dynamic characteristics due to nonstationarity, a forgetting factor is further inserted into the proposed combined algorithm. It is shown that our proposed algorithm, referred to as the FFIKRLS algorithm, provides a clearly compatible algorithm structure and can be improved by the existing modeling techniques from both mapping and weights updating perspectives. Numerical simulations using benchmark Lorenz time series in comparison with existing methods have demonstrated that the proposed algorithm has superior performance in terms of both predictive accuracy and kernel dictionary size.
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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2020.3011892