Time series prediction using bayesian filtering model and fuzzy neural networks

Abstract Time series prediction is a challenging research topic, especially for multi-step-ahead prediction. In this paper, a novel multi-step-ahead time series prediction model is proposed based on combination of the Bayesian filtering model (BFM) and the type-2 fuzzy neural network (FNN). Recently, the studies demonstrate the type-2 FNN model is a promising strategy for multi-step-ahead time series prediction, at the same time, the BFM is an recursion-based sequence information processing approach, which has been used effectively for prediction, filtering and smooth of time series data. In this paper, we consider to use the recursion-based BFM to enhance performance of the FNN-based direct prediction model. A combination model named the BFM2FNN is developed to predict multi-step-ahead time series data. The simulation and comparison results show that the proposed model is more effectiveness and robustness.

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