Fan and Gao proposed “Grey Neural Network model (GNNM(1, N))” and argued that the combined model could improve the prediction accuracy and reduce the computation. forecasted Korean Stock Price Index (KOSPI) by three forecasting models including back-propagation neural network model (BPNN), Bayesian Chiao’s model (BC), and the seasonal autoregressive integrated moving average model (SARIMA).
used a nonlinear time series model to forecast the tendency of the Bel 20 stock market index. Although faced with complicated challenges, the forecast of stock index has still attracted the attention of many industrial experts and scholars. However, the stock price index is influenced by many factors such as the economic situation, policy changes, and emergency. As a barometer of the stock market, the stock index is an important reference for investors to make investment strategies. The stock market is filled with the coexistence of high-risk and high-yield characteristics. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. For a long time, there have been a lot of researches on the forecast of stock index. And the visualization shows the accurate identification of lesion regions from others.The stock index reflects the fluctuation of the stock market. Further, the ablation study empirically shows the effectiveness of each component in our method. We apply our model to the early prediction of peripapillary atrophy and achieve promising results on out-of-distribution test data. Guaranteed by this result, we propose a sequential variational auto-encoder with a reformulated objective function. With personal attributes and disease label respectively provided as side information and supervision, we prove that these disease-related hidden variables can be disentangled from others, implying the avoidance of spurious correlation for generalization to medical data from other (out-of-) distributions. To avoid learning spurious correlation (e.g., confounding bias), we explicitly separate these hidden variables into three parts: a) the disease (clinical)-related part b) the disease (non-clinical)-related part c) others, with only a),b) causally related to the disease however c) may contain spurious correlations (with the disease) inherited from the data provided. Specifically, we introduce the hidden variables which propagate to generate medical data at each time step. We propose a causal hidden Markov model to achieve robust prediction of irreversible disease at an early stage, which is safety-critical and vital for medical treatment in early stages.