Artificial intelligence diagnosis algorithm for expanding a precision expert forecasting system

This study developed a simple and effective expert system for forecasting short-term historical and randomly fluctuating data. This study combined Grey forecasting (GM) and Markov–Fourier Grey forecasting model (MFGM) to develop an expert system of diagnosis by artificial intelligence which improves the effectiveness of forecasting randomly fluctuating data. The contribution of this study is its simple and effective artificial intelligence diagnosis algorithm approach to developing a computer environment for an expert forecasting system which accurately predicts short-term historical and stochastic volatility data.