A forecasting method based on extrema mean empirical mode decomposition and wavelet neural network

Time series forecasting is a widely and important research area in signal processing and machine learning. With the development of the artificial intelligence (AI), more and more AI technologies are used in time series forecasting. Multi-layer network structure has been widely used for forecasting problems. In this paper, based on a data-driven and adaptive method, extrema mean empirical mode decomposition, we proposed a decomposition-forecasting-ensemble approach to time series forecasting. Experimental result shows the prediction result by proposed models are better than original signal and EMD based models.