An optimized twin support vector regression algorithm enhanced by ensemble empirical mode decomposition and gated recurrent unit

Despite the rapid development of support vector regression (SVR), it costs unacceptable training time in large-scale datasets and is hard to fit complex, high frequency oscillating, and non-stationary time series data. SVRs are still perplexed by the selection of critical parameters and hidden noise...

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Vydáno v:	Information sciences Ročník 598; s. 101 - 125
Hlavní autoři:	Ding, Shifei, Zhang, Zichen, Guo, Lili, Sun, Yuting
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Inc 01.06.2022
Témata:	Cloud theory Ensemble empirical mode decomposition (EEMD) Gated recurrent unit (GRU) Salp swarm algorithm (SSA) Twin support vector regression (TWSVR) Cloud theory Gated recurrent unit (GRU) Ensemble empirical mode decomposition (EEMD) Twin support vector regression (TWSVR) Salp swarm algorithm (SSA)
ISSN:	0020-0255, 1872-6291
On-line přístup:	Získat plný text
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