Accurate Stock Price Forecasting Based on Deep Learning and Hierarchical Frequency Decomposition
Author:
Affiliation:
1. School of Economics and Management, Weifang University, Weifang, China
2. School of Economics and Management, Weifang University of Science and Technology, Weifang, China
3. School of Computer Engineering, Weifang University, Weifang, China
Funder
Shandong Provincial Natural Science Foundation
Shandong Provincial Key Research Project on Financial Applications
Doctoral Research Foundation of Weifang University
Doctoral Research Foundation of Weifang University of Science and Technology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10488422.pdf?arnumber=10488422
Reference42 articles.
1. ARMA(p,q) type high order fuzzy time series forecast method based on fuzzy logic relations
2. Forecasting method of stock market volatility in time series data based on mixed model of ARIMA and XGBoost
3. Forecasting stock price volatility: New evidence from the GARCH-MIDAS model
4. STOCK MARKET PREDICTION IN BRICS COUNTRIES USING LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORK HYBRID MODELS
5. A novel (U)MIDAS-SVR model with multi-source market sentiment for forecasting stock returns
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