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Amir Atiya

Personal Details

First Name:Amir
Middle Name:
Last Name:Atiya
Suffix:
RePEc Short-ID:pat15
http://www.alumni.caltech.edu/~amir
21 Shehab street, #14 Mohandesin, Cairo Egypt
00-20-2-335-4773

Research output

as
Jump to: Working papers Articles

Working papers

  1. A.F. Atiya & A.G. Parlos & L. Ingber, 2003. "A reinforcement learning method based on adaptive simulated annealing," Lester Ingber Papers 03rl, Lester Ingber.

Articles

  1. Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.
  2. Amir Atiya & Steve Wall, 2009. "An analytic approximation of the likelihood function for the Heston model volatility estimation problem," Quantitative Finance, Taylor & Francis Journals, vol. 9(3), pages 289-296.
  3. Robert R. Andrawis & Amir F. Atiya, 2009. "A new Bayesian formulation for Holt's exponential smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(3), pages 218-234.

Citations

Many 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.

Working papers

  1. A.F. Atiya & A.G. Parlos & L. Ingber, 2003. "A reinforcement learning method based on adaptive simulated annealing," Lester Ingber Papers 03rl, Lester Ingber.

    Cited by:

    1. Cheng-Ming Lee & Chia-Nan Ko, 2016. "Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network," Energies, MDPI, Open Access Journal, vol. 9(12), pages 1-15, November.
    2. L. Ingber, 2012. "Adaptive simulated annealing," Lester Ingber Papers 12as, Lester Ingber.
    3. L. Ingber, 2007. "Ideas by Statistical Mechanics (ISM)," Lester Ingber Papers 07ji, Lester Ingber.

Articles

  1. Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.

    Cited by:

    1. Anders Bredahl Kock & Timo Teräsvirta, 2011. "Forecasting performance of three automated modelling techniques during the economic crisis 2007-2009," CREATES Research Papers 2011-28, Department of Economics and Business Economics, Aarhus University.
    2. Fischer, Thomas & Krauss, Christopher & Treichel, Alex, 2018. "Machine learning for time series forecasting - a simulation study," FAU Discussion Papers in Economics 02/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    3. Akın, Melda, 2015. "A novel approach to model selection in tourism demand modeling," Tourism Management, Elsevier, vol. 48(C), pages 64-72.
    4. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "“Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting”," AQR Working Papers 201701, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2017.
    5. Wu, Shaomin & Akbarov, Artur, 2011. "Support vector regression for warranty claim forecasting," European Journal of Operational Research, Elsevier, vol. 213(1), pages 196-204, August.
    6. Neil Kellard & Denise Osborn & Jerry Coakley & Imanol Arrieta-ibarra & Ignacio N. Lobato, 2015. "Testing for Predictability in Financial Returns Using Statistical Learning Procedures," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 672-686, September.
    7. Zaher Mundher Yaseen & Mazen Ismaeel Ghareb & Isa Ebtehaj & Hossein Bonakdari & Ridwan Siddique & Salim Heddam & Ali A. Yusif & Ravinesh Deo, 2018. "Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 105-122, January.
    8. Green, Gareth & Richards, Timothy, 2016. "Interpreting Results of Demand Estimation from Machine Learning Models," 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts 236147, Agricultural and Applied Economics Association.
    9. Cui, Can & Wu, Teresa & Hu, Mengqi & Weir, Jeffery D. & Li, Xiwang, 2016. "Short-term building energy model recommendation system: A meta-learning approach," Applied Energy, Elsevier, vol. 172(C), pages 251-263.
    10. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 672-688, July.
    11. Advait Sarkar & Neal Lathia & Cecilia Mascolo, 2015. "Comparing cities’ cycling patterns using online shared bicycle maps," Transportation, Springer, vol. 42(4), pages 541-559, July.
    12. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
    13. Tzai-Shuen Chen, 2018. "Evaluating Conditional Cash Transfer Policies with Machine Learning Methods," Papers 1803.06401, arXiv.org.

  2. Amir Atiya & Steve Wall, 2009. "An analytic approximation of the likelihood function for the Heston model volatility estimation problem," Quantitative Finance, Taylor & Francis Journals, vol. 9(3), pages 289-296.

    Cited by:

    1. Robert Azencott & Yutheeka Gadhyan & Roland Glowinski, 2014. "Option Pricing Accuracy for Estimated Heston Models," Papers 1404.4014, arXiv.org, revised Jul 2015.

  3. Robert R. Andrawis & Amir F. Atiya, 2009. "A new Bayesian formulation for Holt's exponential smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(3), pages 218-234.

    Cited by:

    1. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    2. Mirko Kremer & Brent Moritz & Enno Siemsen, 2011. "Demand Forecasting Behavior: System Neglect and Change Detection," Management Science, INFORMS, vol. 57(10), pages 1827-1843, October.

More information

Research fields, statistics, top rankings, if available.

Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-EVO: Evolutionary Economics (2) 2003-09-24 2004-07-18
  2. NEP-CMP: Computational Economics (1) 2004-07-18
  3. NEP-LAB: Labour Economics (1) 2004-07-18

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