Optimal partition algorithm of the RBF neural network and its application to financial time series forecasting

2004 ◽  
Vol 14 (1) ◽  
pp. 36-44 ◽  
Author(s):  
Y. F. Sun ◽  
Y. C. Liang ◽  
W. L. Zhang ◽  
H. P. Lee ◽  
W. Z. Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Financial Time Series ◽  
Rbf Neural Network ◽  
Time Series Forecasting ◽  
Optimal Partition ◽  
Financial Time

Financial Time Series Forecasting with Multi-Modality Graph Neural Network

2021 ◽  
pp. 108218
Author(s):  
Dawei Cheng ◽  
Fangzhou Yang ◽  
Sheng Xiang ◽  
Jin Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Financial Time Series ◽  
Time Series Forecasting ◽  
Financial Time

Improved RBF neural network algorithm in financial time series prediction

2021 ◽  
pp. 1-9
Author(s):  
Jian Zhu ◽  
Haiming Long ◽  
Saihong Liu ◽  
Wenzhi Wu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Stock Market ◽  
Financial Time Series ◽  
Rbf Neural Network ◽  
Time Series Prediction ◽  
Market Price ◽  
Prediction Errors ◽  
Financial Time ◽  
Sample Data

The financial market is often unpredictable and extremely susceptible to political, economic and other factors. How to achieve accurate predictions of financial time series is very important for scientific research and financial enterprise management. Based on this, this article takes the application of the improved RBF neural network(NN) algorithm in financial time series forecasting as the research object, and explores how to use the improved RBF NN algorithm to predict the stock market price, with a view to reducing investment risks and increasing returns for the majority of stock investors to provide help. This article uses the stock market prices of three listed companies in May 2019 as the data samples for this survey, including 72 training sample data and 21 test sample data. These three stocks were predicted by using the improved RBF NN algorithm Experiments, the experimental results show that the prediction errors of the improved RBF NN algorithm for the three stocks are 2.14%, 0.69% and 1.47%, while the traditional RBF NN algorithm’s prediction errors for the stocks are 5.74%, 2.38% and 11.37%. This shows that the improved algorithm is significantly more accurate and more effective than traditional algorithms. Therefore, the application of the improved RBF NN algorithm in financial time series prediction will be more extensive in the future.

AComNN: Attention enhanced Compound Neural Network For financial time-series forecasting with cross-regional features

2021 ◽  
pp. 107649
Author(s):  
Zhen Yang ◽  
Jacky Keung ◽  
Md Alamgir Kabir ◽  
Xiao Yu ◽  
Yutian Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Financial Time Series ◽  
Time Series Forecasting ◽  
Financial Time ◽  
Regional Features
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