A Stock Trend Forecast Algorithm Based on Deep Neural Networks

As a recognized complex dynamic system, the stock market has many influencing factors, such as nonstationarity, nonlinearity, high noise, and long memory. It is difficult to explain it simply through mathematical models. Therefore, the analysis and prediction of the stock market have been a very challenging job since long time. Therefore, this paper adopts an encoder-decoder model of attention mechanism, adding attention mechanism from two aspects of feature and time. Both encoder and decoder use LSTM neural network. This method solves two problems in time series prediction; the first problem is that multiple input features have different degrees of influence on the target sequence, the feature attention mechanism is used to deal with this problem, and the weights of different input features can be obtained. A more robust feature association relationship is obtained; the second problem is that the data before and after the sequence have a strong time correlation. The time attention mechanism is used to deal with this problem, and the weights at different time points can be obtained to obtain more robustness and good timing dependencies. The simulation and experimental results show that the introduction of the attention mechanism can obtain lower forecast errors, which proves the effectiveness of the model in dealing with stock forecasting problems.

Digital Art Feature Association Mining Based on the Machine Learning Algorithm

With the development of computer hardware and software, digital art is a new discipline. It uses computers and digital technology as tools to perform artistic expression. It can be expanded to various binary numerical codes with computers as the center and can also be refined to various categories of creation with computers. The research scope is set in the field of digital art, and all kinds of accidental factors of digital art creation based on the machine learning algorithm are mined and analyzed for feature correlation. Based on the hidden association relationship of massive data, the study focuses on the implicit association mining of digital art features of data for the recommendation algorithm. The classification and continuous data feature attributes are introduced and discretized, and the binary representation of data features is extended to ensure the diversity of data feature attributes. In order to mine some correlation features in data, a heuristic feature mining method based on minimum support was studied to discover the frequency of correlation features and construct the optimal feature subset. Based on the frequent items of data features, this study observes the heuristic algorithm of digital art feature association mining based on minimum confidence and carries out feature matching based on digital art feature association mining under different situation modes. The validity of the proposed algorithm is verified by using the experimental data of health and medical situations in the machine learning library.