A Medium to Long-Term Multi-Influencing Factor Copper Price Prediction Method Based on CNN-LSTM
Author:
Affiliation:
1. School of Computer and Computing Science, Hangzhou City University, Hangzhou, Zhejiang, China
2. School of Electronics and Information Engineering, Tongji University, Shanghai, China
Funder
National Natural Science Foundation of China
Hangzhou Key Laboratory for Internet of Things (IoT) Technology and Application
SuperComputing Center of Hangzhou City University
Zhejiang Engineering Laboratory Intelligent Plant Factory
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10159241.pdf?arnumber=10159241
Reference37 articles.
1. An adaptive forecasting approach for copper price volatility through hybrid and non-hybrid models
2. Forecasting commodity price indexes using macroeconomic and financial predictors
3. Stock return prediction under GARCH — An empirical assessment
4. Freezing copper as a noble metal–like catalyst for preliminary hydrogenation
5. Forecasting Short-Term Oil Price with a Generalised Pattern Matching Model Based on Empirical Genetic Algorithm
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