Using dynamic mode decomposition to extract cyclic behavior in the stock market
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DOI: 10.1016/j.physa.2015.12.059
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References listed on IDEAS
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Cited by:
- Elmore, Clay T. & Dowling, Alexander W., 2021. "Learning spatiotemporal dynamics in wholesale energy markets with dynamic mode decomposition," Energy, Elsevier, vol. 232(C).
- Jinxiang Xi & Weizhong Zhao, 2019. "Correlating exhaled aerosol images to small airway obstructive diseases: A study with dynamic mode decomposition and machine learning," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-22, January.
- Ausloos, Marcel & Cerqueti, Roy & Bartolacci, Francesca & Castellano, Nicola G., 2018.
"SME investment best strategies. Outliers for assessing how to optimize performance,"
Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 754-765.
- Marcel Ausloos & Roy Cerqueti & Francesca Bartolacci & Nicola G. Castellano, 2018. "SME investment best strategies. Outliers for assessing how to optimize performance," Papers 1807.09583, arXiv.org.
- Jinxiang Xi & Xiuhua April Si, 2018. "Review of Feature Extraction from Exhaled Aerosol Fingerprints to Diagnose Lung Structural Remolding," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 11(3), pages 8504-8508, November.
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Keywords
Econophysics; Business cycle; Stock markets; Koopman operator; Dynamic mode decomposition; Complex system;All these keywords.
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