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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Vydané v:	IEEE signal processing letters Ročník 27; s. 1
Hlavní autori:	Guo, Jinhua, Chen, Hao, Chen, Songhang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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