A Mega-Trend-Diffusion and Monte Carlo based virtual sample generation method for small sample size problem

Data-driven modeling has attracted wide attention in academia because of its effectiveness. However, Due to the lack of data, some traditional modeling methods, such as extreme learning machine (ELM), can't achieve high learning accuracy. A novel approach based on Mega-Trend-Diffusion (MTD) and Monte Carlo is presented in this paper to deal with the problem, named Monte Carlo Mega-Trend-Diffusion (MCMTD). The proposed approach utilizes MTD to estimate the acceptable range of the attributions and Latin hypercube sampling method to sample. ELM is employed to establish the prediction model. In this paper, two real data sets, the multi-layer ceramic capacitors (MLCC) and the purified terephthalic acid (PTA), are used to verify the effectiveness and reasonability of MCMTD. The experimental results show that MCMTD can significantly enhance the accuracy and ability of the forecasting model.