A New Direct Multi-step Ahead Prediction Model Based on EMD and Chaos Analysis
基于经验模式分解与混沌分析的直接多步预测模型

The direct multi-step ahead prediction model, which employs observation values and does not depend on the result of single-step prediction, provides more accurate prediction than indirect model. But in this case, the model could be asked to learn various object functions. In this paper, a hybrid model is presented based on empirical mode decomposition (EMD) and chaos analysis. The model employs EMD to decompose the original sequences into many basic modal partitions which can significantly represent potential information of original time series. And chaos features of those data sequences can be used to design DRNN. By these means, the model can be improved to learn various objective functions. And then, more precious prediction can be obtained. Finally, a benchmark time series is tested to display the advantage of this model.