In this paper we are reviewing neural network and data mining in stock market prediction, because employing traditional methods for the prediction failed to ensure the reliability. NN is one of the well known techniques which extract useful information from large data sets, whereas data mining ...
This paper also provides the effect of various topological parameters on the accuracy and training time of neural networks. A topology of neural network is proposed for the prediction of Indian stock market index S&P CNX NIFTY.doi:10.1080/02564602.2006.11657936Rihani, VGarg, Sanjeev Kumar...
In this system, a neural network learned the relationship between various technical and economic indexes and the timing for when to buy and sell stocks on the German Stock Exchange. The results obtained lead to the conclusion that neural networks can be considered as useful instruments to improve ...
EASSY 1 Temporal Relational Ranking for Stock Prediction This eassy contributed a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction. The key novelty of this work is the proposal of a new component in neural network modeling, named Temporal Graph Convolution, ...
现代机器学习:with the booming of artificial intelligence technology, machine learning techniques have been introduced to handle complex financial market data and proved to be useful for making stock trendpredictions。 第三段:CNN简介 —— 近些年来使用图像特征的研究 —— 指出现在的不足就是欠缺考虑整个...
A DNN-based prediction model is designed based on the PSR method and a long- and short-term memory networks (LSTMs) for DL and used to predict stock prices. The proposed and some other prediction models are used to predict multiple stock indices for different periods. A comparison of the ...
Tapas Kumar Patra.: Indian Stock Market Prediction Using Differential Evolutionary Neural Network Model - Mohapatra, Raj () Citation Context ...ater discrimination capability in the input pattern space, fast convergence Fuzzy sets can be filled with suitable relations that will be capable of detecting...
This project uses machine learning methods to solve the problem of stock market prediction. The project uses the Shanghai Stock Exchange 000001, China Ping An stock (code SZ_000001) from an open-source stock data center and trains it using LSTM (Long Short-Term Memory Neural Network) which ...
(LS-SVMR). We apply the five models to make price prediction of three individual stocks, namely, Bank of China, Vanke A and Kweichou Moutai. Adopting mean square error and average absolute percentage error as criteria, we find BP neural network consistently and robustly outperforms the other ...
Stock markets are dynamic systems that exhibit complex intra-share and inter-share temporal dependencies. Spatial-temporal graph neural networks (ST-GNN) are emerging DNN architectures that have yielded high performance for flow prediction in dynamic sys