Juan Jose Flores
|[This author has chosen not to make the email address public]|
Universidad Michoacana de San Nicolas de Hidalgo (University of Michoacan)http://www.umich.mx
Research outputJump to: Articles Chapters
- Flores, Juan J. & Graff, Mario & Rodriguez, Hector, 2012. "Evolutive design of ARMA and ANN models for time series forecasting," Renewable Energy, Elsevier, vol. 44(C), pages 225-230.
- González Santoyo, Federico & Flores Romero, Beatriz & Chagolla Farías, Mauricio & Flores, Juan J., 2004. "Uncertainty Theory Applied to Optimal Selection of Personnel in an Enterprise," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(2), pages 75-92, November.
- Flores, Juan & González, Federico & Flores, Beatriz, 2001.
"Qualitative/Quantitative Financial Analysis,"
Fuzzy Economic Review,
International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(2), pages 75-86, November.
RePEc:wsi:ijmpcx:v:21:y:2010:i:04:p:489-501 is not listed on IDEAS
- F. González Santoyo & J. Flores Romero & B. Flores Romero & J. Mendoza Ramírez, 2001. "Multiple Fuzzy Irr In The Financial Decision Environment," World Scientific Book Chapters,in: Fuzzy Sets In Management, Economics And Marketing, chapter 15, pages 223-237 World Scientific Publishing Co. Pte. Ltd..
CitationsMany of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.
- Flores, Juan J. & Graff, Mario & Rodriguez, Hector, 2012.
"Evolutive design of ARMA and ANN models for time series forecasting,"
Elsevier, vol. 44(C), pages 225-230.
- Wang, Jianzhou & Hu, Jianming, 2015. "A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vec," Energy, Elsevier, vol. 93(P1), pages 41-56.
- Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
- Shao, Zhen & Gao, Fei & Yang, Shan-Lin & Yu, Ben-gong, 2015. "A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: Hidden characteristic extraction and probability density prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 876-889.
- Wang, Siyan & Sun, Xun & Lall, Upmanu, 2017. "A hierarchical Bayesian regression model for predicting summer residential electricity demand across the U.S.A," Energy, Elsevier, vol. 140(P1), pages 601-611.
- Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
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