1. Home
2. Premium
3. IEEE ICASSP 2020 Virtual Conference May 2020
4. Stock Movement Prediction That Integrates Heterogeneous Data Sources Using Dilated Causal Convolution Networks With Attention

Stock Movement Prediction That Integrates Heterogeneous Data Sources Using Dilated Causal Convolution Networks With Attention

Collection:

IEEE ICASSP 2020 Virtual Conference May 2020

The purpose of this research is to develop a high performing model for stock movement prediction utilizing financial indicators and news data. Until recently, the majority of prediction models have employed only the financial indicators, but they possess

• IEEE MemberUS $11.00
• Society MemberUS $0.00
• IEEE Student MemberUS $11.00
• Non-IEEE MemberUS $15.00

Purchase

Videos in this product

Stock Movement Prediction That Integrates Heterogeneous Data Sources Using Dilated Causal Convolution Networks With Attention

00:19:05

0 views

The purpose of this research is to develop a high performing model for stock movement prediction utilizing financial indicators and news data. Until recently, the majority of prediction models have employed only the financial indicators, but they possess