Abstract
Yield prediction has always been the focus of stock investors' attention in stock investment. This research uses the virtual stock scene constructed by VR technology to explore the mathematical model of return rate prediction. First, neural nodes, activation functions and output functions are designed under the LSTM framework, and a recurrent neural network that can handle long-term sequences is constructed. Use VR technology to simulate stock data and extract key frames from the image sequence. Aiming at the timing of key curve nodes, this study uses a long and short-term memory neural network that is good at processing time series data to classify curve trends, and realizes the identification of abnormal fluctuations in virtual scenes of stock images. Then, according to the existing probability control asset allocation theory, the index of investment allocation is adjusted from the entire time series to a dynamic time series. According to the stock price decomposition of EEMD, the EEMD-LSTM model is constructed to predict the stock market price. Use EEMD to decompose the original sequence to obtain IMF and trend items containing various time-scale characteristics. Then, the LSTM neural network is used to predict and analyze each rate of return, and the analysis software Python is used to process and count the predicted results. At the same time, retrospective restrictions are regarded as riskier assets. Finally, the investment is made under conditions that do not allow shorting in the VR simulation scenario. The accuracy of the calculation is more than 70% except for the Shanghai and Shenzhen 300 Index reaching 66%. The research results show that in the case of different stock price forecasts, the prediction ability of LSTM neural network has been greatly improved compared with that of support vector machine, which fully reflects the advantages of LSTM in time series prediction.
Highlights
In the economic system and social organization of modern society, the financial market occupies an important position [1]
In order to improve the prediction accuracy of dissolved oxygen in aquaculture, Chen proposed a hybrid model based on principal component analysis (PCA) and long short-term memory (LSTM) neural network to predict the content of dissolved oxygen in aquaculture
This study introduces the graphical function of stock price forecasting
Summary
In the economic system and social organization of modern society, the financial market occupies an important position [1]. Bai proposed an integrated long and short-term memory neural network (E-LSTM) for hourly PM2.5 concentration prediction The realization of this model is divided into three steps. In order to improve the prediction accuracy of dissolved oxygen in aquaculture, Chen proposed a hybrid model based on principal component analysis (PCA) and long short-term memory (LSTM) neural network to predict the content of dissolved oxygen in aquaculture. Due to the lack of dictionary and computational cost of training, it suffers from connectionist temporal classification and attention-based RNN [9] He explored contemporary acoustic models of long-term short-term memory and gated recurrent neural networks. Graphical features may contain information that is difficult to express in numerical features
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