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• W1969765329 abstract "Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=TeX>$s$ </tex-math></inline-formula> prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets." @default.
• W1969765329 created "2016-06-24" @default.
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• W1969765329 date "2014-05-01" @default.
• W1969765329 modified "2023-10-16" @default.
• W1969765329 title "PSO-MISMO Modeling Strategy for MultiStep-Ahead Time Series Prediction" @default.
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• W1969765329 doi "https://doi.org/10.1109/tcyb.2013.2265084" @default.
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