Fig. 5. Predictions on gene expressions, the stock index, and patient admissions.

a Based on the ARNN framework, the dynamical trends of gene expressions in rats were accurately predicted for six circadian rhythm-related genes, i.e., Nr1d1, Arntl, Pfkm, RGD72, Per2, and Cry1. In each prediction, the inputs included the expressions from the initial m = 16 time points, and the outputs of the multistep-ahead prediction were the expressions for L − 1 = 6 time points ahead. b On the basis of D = 1130 stock indices of the Shanghai Stock Exchange, the short-term trend of the B-Share Index was predicted, which shows that ARNN achieves relatively high accuracy and strong correlation with the real value. c ARNN predicted the dynamical trend of daily cardiovascular disease admissions. The time series ranging from 0 to 130 days were regarded as known information/input, and ARNN predicted the admissions for the L − 1 = 60 days ahead. We also compared the ARNN results with the other prediction results for each dataset, which are shown in gray curves. Among the nine prediction methods, the performance of ARNN is the best.