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A hybrid forecasting model that integrates ensemble empirical model decomposition (EEMD), and extreme learning machine (ELM) for computer products sales is proposed. The EEMD is a new piece of signal processing technology. It is based on the local characteristic time scales of a signal and could decompose the complicated signal into intrinsic mode functions (IMFs). The ELM is a novel learning algorithm for single-hidden-layer feedforward networks. In our proposed approach, the initial task is to apply the EEMD method to decompose the original sales data into a number of IMFs. The hidden useful information of the original data could be discovered in those IMFs. The IMFs are then integrated with the ELM method to develop an effective forecasting model for computer products sales. Experimental results from three real computer products sales data, including hard disk, display card, and notebook, showed that the proposed hybrid sales forecasting method outperforms the four comparative models and is an effective alternative for forecasting sales of computer products.

Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone's health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR), artificial neural network (ANN), multivariate adaptive regression splines (MARS), and support vector regression (SVR) techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models.

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