Abstract
The rapid development of edge computing drives the rapid development of stock market prediction service in terminal equipment. However, the traditional prediction service algorithm is not applicable in terms of stability and efficiency. In view of this challenge, an improved Elman neural network is proposed in this paper. Elman neural network is a typical dynamic recurrent neural network that can be used to provide the stock price prediction service. First, the prediction model parameters and build process are analysed in detail. Then, the historical data of the closing price of Shanghai composite index and the opening price of Shenzhen composite index are collected for training and testing, so as to predict the prices of the next trading day. Finally, the experiment results validate that it is effective to predict the short-term future stock price by using the improved Elman neural network model.
Highlights
In order to apply traditional stock prediction algorithms to terminal devices such as edge computing and mobile phones, we build a stock price prediction model based on an improved Elman network with the aim to predict the stock price simpler and more stable
To analyse the new algorithm model more clearly, we quantitatively analysed the performance of the model with a variety of mathematical tools and error analysis methods
Elman neural network is a typical feedback neural network model widely used, which is generally divided into four layers: input layer, hidden layer, bearing layer, and output layer [5, 6]
Summary
Elman neural network is a typical feedback neural network model widely used, which is generally divided into four layers: input layer, hidden layer, bearing layer, and output layer [5, 6]. Ere are linear and nonlinear excitation functions in the hidden layer element, and the excitation function usually takes the nonlinear function of Sigmoid. Elman neural network adds a bearing layer to the hidden layer as a one-step delay operator to achieve the purpose of memory so that the system has the ability to adapt to time-varying characteristics and enhance the global stability of the network [7,8,9]. We will use the same model for training and testing based on these two datasets
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