Title :
PSO-MISMO Modeling Strategy for MultiStep-Ahead Time Series Prediction
Author :
Yukun Bao ; Tao Xiong ; Zhongyi Hu
Author_Institution :
Sch. of Manage., Huazhong Univ. of Sci. & Technol., Wuhan, China
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 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.
Keywords :
MIMO systems; genetic algorithms; neural nets; particle swarm optimisation; time series; PSO-MISMO modeling strategy; equal-size s prediction horizons; genetic algorithm; heuristic; multiple-input several multiple-output modeling strategy; multistep-ahead time series prediction; neural networks; particle swarm optimization; prediction horizons; self-adaptive mode; Genetic algorithm; multiple-output models; multistep-ahead time series prediction; particle swarm optimization;
Journal_Title :
Cybernetics, IEEE Transactions on
DOI :
10.1109/TCYB.2013.2265084