Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting Method.
arXiv: Neural and Evolutionary Computing(2018)
摘要
A newly introduced method called Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting method is applied and extended in this study to forecast numerical values. Unlike traditional forecasting techniques which forecast only future values, our proposed method provides a new extension to correct the predicted values which is done by forecasting the estimated error. Experimental results demonstrated that the proposed method has a high accuracy both in training and testing data and outperform the state-of-the-art RNN models on Mackey-Glass, NARMA, Lorenz and Henon map datasets.
更多查看译文
关键词
Recurrent Neural Networks, Taylor, Forecasting, Time series, Error estimation, Exponential smoothed method
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络