Paper Info
Title
Prediction of Short-Term Stock Price Trend Based on Multiview RBF Neural Network
Abstract
Stock price prediction is important in both financial and commercial domains, and using neural networks to forecast stock prices has been a topic of ongoing research and development. Traditional prediction models are often based on a single type of data and do not account for the interplay of many variables. This study covers a radial basis neural network modeling technique with multiview collaborative learning capabilities for incorporating the impacts of numerous elements into the prediction model. This research offers a multiview RBF neural network prediction model based on the classic RBF network by integrating a collaborative learning item with multiview learning capabilities (MV-RBF). MV-RBF can make full use of both the internal information provided by the correlation between each view and the distinct characteristics of each view to form independent sample information. By using two separate stock qualities as input feature information for trials, this study proves the viability of the multiview RBF neural network prediction model on a real data set.
Year
DOI
Venue
2021
10.1155/2021/8495288
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
DocType
Volume
ISSN
Journal
2021
1687-5265
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Bailin Lv100.34
Yizhang Jiang238227.24