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A compressed sensing based AI learning paradigm for crude oil price forecasting

  • Published:2014-11 Issue: Volume:46 Page:236-245
  • ISSN:0140-9883
  • Container-title:Energy Economics
  • language:en
  • Short-container-title:Energy Economics

Author:

Yu Lean,Zhao Yang,Tang Ling

Funder

National Science Fund for Distinguished Young Scholars

National Natural Science Foundation of China

National Program for Support of Top-Notch Young professionals

Fundamental Research Funds for the Central Universities in BUCT

Publisher

Elsevier BV

Subject

General Energy,Economics and Econometrics

Reference51 articles.

1. Discrete cosine transform;Ahmed;IEEE Trans. Comput.,1974

2. A hybrid artificial intelligence approach to monthly forecasting of crude oil price time series;Amin-Naseri,2007

3. Multi-step-ahead time series prediction using multiple-output support vector regression;Bao;Neurocomputing,2014

4. PSO-MISMO modeling strategy for multi-step-ahead time series prediction;Bao;IEEE Trans. Cybern.,2014

5. Measuring business cycles: approximate band-pass filters for economic time series;Baxter;Rev. Econ. Stat.,1999

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