Time series prediction using dynamic Bayesian network
| Autor: | Qinkun Xiao, Chu Chaoqin, Zhao Li |
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| Rok vydání: | 2017 |
| Předmět: |
Sequence
010504 meteorology & atmospheric sciences Artificial neural network Computer science Recursion (computer science) 02 engineering and technology Kalman filter computer.software_genre 01 natural sciences Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials Robustness (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Electrical and Electronic Engineering Time series Echo state network computer Dynamic Bayesian network 0105 earth and related environmental sciences |
| Zdroj: | Optik. 135:98-103 |
| ISSN: | 0030-4026 |
| Popis: | 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 Kalman filtering model (KFM) and the echo neural networks (ESN). Recently, the studies demonstrate the ESN model is a promising strategy for multi-step-ahead time series prediction, at the same time, the KFM is a 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 KFM to enhance performance of the ESN-based direct prediction model. A novel graph model named the E-KFM that generated from combination of the ESN and the KFM 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. |
| Databáze: | OpenAIRE |
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