دانلود رایگان مقاله انگلیسی پیش بینی رده زمانی با استفاده از شبکه بیزی پویا - الزویر 2017

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 Kalman filtering model (KFM) and the echo neural networks (ESN). Recently, the studies demonstrate the ESN model is a promising strategy for multistep-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.

4. Conclusions

In this paper, a multi-step-ahead time series prediction models that combine ESN with the KFM is proposed. Our contribution can be described as: (1) We propose a novel graph-based time series prediction model named the E-KFM,the model combines the neural network and Bayesian inference together effectively, and uses recursion-based method to predict multi-step-ahead time series data. (2) Based on some theories, such as Bayesian rules, dynamic Bayesian networks and phase space reconstruction, the probability-based recursion calculation structure is presented to obtain the higher accuracy of multi-step-ahead prediction. Experimental results show that the E-KFM model has better performance than some existing algorithms.