2022
DOI: 10.1007/s11227-022-04646-6
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Particle swarm optimization-based empirical mode decomposition predictive technique for nonstationary data

Abstract: Real-world nonstationary data are usually characterized by high nonlinearity and complex patterns due to the effects of different exogenous factors that make prediction a very challenging task. An ensemble strategically combines multiple techniques and tends to be robust and more precise compared to a single intelligent algorithmic model. In this work, a dynamic particle swarm optimization-based empirical mode decomposition ensemble is proposed for nonstationary data prediction. The proposed ensemble implement… Show more

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Cited by 2 publications
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