  Multi-step-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 multi-step-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 study 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
s 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.
