An Improved Elman Network for Stock Price Prediction Service

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.

A New ANN-Particle Swarm Optimization with Center of Gravity (ANN-PSOCoG) Prediction Model for the Stock Market under the Effect of COVID-19

“…Using formula (7), F cg can be determined similar to the sum of gravities G in formula (5). e fitness value F(x i ) for maximum optimization has been given in formula (8), while formula ( 9) is used for minimum optimization:…”

“…Also, we used historical data of DJIA dataset where the overall number of observations for the exchange indices was 9036 trading days, from Jan 28, 1985, to Dec 02, 2020. Each observation includes the opening price, highest price, lowest price, the (3) Run NN corresponding to each particle; (4) while (termination condition is not met) (5) fori � 1 to pop (6) Compute the fitness value (f i ) for each particle; (7) if (the fitness value is less than P il ) ( 8)…”

“…e traditional stock forecasting methods could not fit and analyze the highly nonlinear and multifactor stock market well, so there are low prediction accuracy and slow training speed problems [7]. erefore, recent studies such as fusion in stock market prediction [6], financial trading strategy system [8], and short-term stock market price trend predictions [9] try to find solutions to problems.…”

Öğrenen ve Öngören Varlık Yonetimi

“…Havuzlama türlerinden sıklıkla kullanılan ortalama havuzlamada, girdi yine maksimum havuzlamadaki gibi parçalara ayrılarak her bir parçadaki değerlerin ortalaması alınır. Elman sinir ağı, sistemin zamanla değişen özelliklere uyum sağlama, ağın kararlılığını geliştirme yeteneğine sahip olması ve bellek amacına ulaşmak için tek adımlı bir gecikme operatörü olarak gizli katmana bir yatak katmanı ekler [22].…”

“…Çıktımız ara katmandaki ilgili nöronun çıktısı olacaktır. Ara katman elemanında doğrusal ve doğrusal olmayan uyarı fonksiyonları vardır ve uyarı fonksiyonu genellikle sigmoid doğrusal olmayan fonksiyonunu alır [22]. Bu çalışmada hem türevinin kolay alınması hem de güncel çalışmalarda çoğunlukla kullanılan sigmoid ve RELU aktivasyon fonksiyonları kullanılmaktadır.…”