Monica Adya

Personal Details

First Name:	Monica
Middle Name:
Last Name:	Adya
Suffix:
RePEc Short-ID:	pad55

Affiliation

College of Business Administration
Marquette University

Milwaukee, Wisconsin (United States)
http://www.busadm.mu.edu/
RePEc:edi:cbamuus (more details at EDIRC)

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.

Articles

1. Adya, Monica & Collopy, Fred & Armstrong, J. Scott & Kennedy, Miles, 2001. "Automatic identification of time series features for rule-based forecasting," International Journal of Forecasting, Elsevier, vol. 17(2), pages 143-157.

   Cited by:

   1. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
   2. Edward J. Lusk, 2019. "Time Series Forecasting in Stock Trading Markets: The Turning Point Curiosity," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 8(4), pages 01-16, July.
   3. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
   4. Han, Weiwei & Wang, Xun & Petropoulos, Fotios & Wang, Jing, 2019. "Brain imaging and forecasting: Insights from judgmental model selection," Omega, Elsevier, vol. 87(C), pages 1-9.
   5. Manuel Bern & Edward Lusk, 2020. "The Reduced Rules Rule Based Forecasting Decision Support System: Details and Functionalities: An Audit Context," Accounting and Finance Research, Sciedu Press, vol. 9(3), pages 1-13, August.
   6. Gerunov, Anton, 2016. "Automating Analytics: Forecasting Time Series in Economics and Business," MPRA Paper 71010, University Library of Munich, Germany.
      • Anton Antonov GERUNOV, 2016. "Automating Analytics: Forecasting Time Series in Economics and Business," Journal of Economics and Political Economy, KSP Journals, vol. 3(2), pages 340-349, June.
   7. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
   8. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
   9. Fotios Petropoulos & Enno Siemsen, 2023. "Forecast Selection and Representativeness," Management Science, INFORMS, vol. 69(5), pages 2672-2690, May.
   10. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
   11. JS Armstrong, 2004. "Forecasting for Environmental Decision Making," General Economics and Teaching 0412023, University Library of Munich, Germany.
       • J. S. Armstrong, 2005. "Forecasting for Environmental Decision Making," General Economics and Teaching 0502017, University Library of Munich, Germany.
   12. Fildes, Robert & Petropoulos, Fotios, 2015. "Simple versus complex selection rules for forecasting many time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1692-1701.
   13. James W. Taylor, 2004. "Smooth transition exponential smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 385-404.
   14. Thiyanga S. Talagala & Feng Li & Yanfei Kang, 2019. "Feature-based Forecast-Model Performance Prediction," Monash Econometrics and Business Statistics Working Papers 21/19, Monash University, Department of Econometrics and Business Statistics.
   15. Fildes, Robert & Petropoulos, Fotios, 2013. "An evaluation of simple forecasting model selection rules," MPRA Paper 51772, University Library of Munich, Germany.
   16. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
   17. Graff, Mario & Peña, Rafael & Medina, Aurelio & Escalante, Hugo Jair, 2014. "Wind speed forecasting using a portfolio of forecasters," Renewable Energy, Elsevier, vol. 68(C), pages 550-559.
   18. Thiyanga S Talagala & Rob J Hyndman & George Athanasopoulos, 2018. "Meta-learning how to forecast time series," Monash Econometrics and Business Statistics Working Papers 6/18, Monash University, Department of Econometrics and Business Statistics.
   19. Thomassey, Sebastien & Happiette, Michel & Castelain, Jean Marie, 2005. "A short and mean-term automatic forecasting system--application to textile logistics," European Journal of Operational Research, Elsevier, vol. 161(1), pages 275-284, February.
   20. Manuel R. Arahal & Manuel G. Ortega & Manuel G. Satué, 2021. "Chiller Load Forecasting Using Hyper-Gaussian Nets," Energies, MDPI, vol. 14(12), pages 1-15, June.
   21. Talagala, Thiyanga S. & Li, Feng & Kang, Yanfei, 2022. "FFORMPP: Feature-based forecast model performance prediction," International Journal of Forecasting, Elsevier, vol. 38(3), pages 920-943.
   22. Adya, Monica & Armstrong, J. Scott & Collopy, Fred & Kennedy, Miles, 2000. "An application of rule-based forecasting to a situation lacking domain knowledge," International Journal of Forecasting, Elsevier, vol. 16(4), pages 477-484.
   23. Fildes, Robert & Madden, Gary & Tan, Joachim, 2007. "Optimal forecasting model selection and data characteristics," MPRA Paper 10819, University Library of Munich, Germany.

2. Adya, Monica & Armstrong, J. Scott & Collopy, Fred & Kennedy, Miles, 2000. "An application of rule-based forecasting to a situation lacking domain knowledge," International Journal of Forecasting, Elsevier, vol. 16(4), pages 477-484.

   Cited by:

   1. Sanders, Nada R. & Manrodt, Karl B., 2003. "The efficacy of using judgmental versus quantitative forecasting methods in practice," Omega, Elsevier, vol. 31(6), pages 511-522, December.
   2. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
   3. Hamulczuk, Mariusz & Grudkowska, Sylwia & Gędek, Stanisław & Klimkowski, Cezary & Stańko, Stanisław, 2013. "Essential econometric methods of forecasting agricultural commodity prices," Multiannual Program Reports 164834, Institute of Agricultural and Food Economics - National Research Institute (IAFE-NRI).
   4. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
      • Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660, July.
   5. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
   6. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
   7. Adya, Monica & Collopy, Fred & Armstrong, J. Scott & Kennedy, Miles, 2001. "Automatic identification of time series features for rule-based forecasting," International Journal of Forecasting, Elsevier, vol. 17(2), pages 143-157.
   8. Arora, Siddharth & Taylor, James W., 2018. "Rule-based autoregressive moving average models for forecasting load on special days: A case study for France," European Journal of Operational Research, Elsevier, vol. 266(1), pages 259-268.
   9. Graff, Mario & Peña, Rafael & Medina, Aurelio & Escalante, Hugo Jair, 2014. "Wind speed forecasting using a portfolio of forecasters," Renewable Energy, Elsevier, vol. 68(C), pages 550-559.
   10. Talagala, Thiyanga S. & Li, Feng & Kang, Yanfei, 2022. "FFORMPP: Feature-based forecast model performance prediction," International Journal of Forecasting, Elsevier, vol. 38(3), pages 920-943.

3. Adya, Monica, 2000. "Corrections to rule-based forecasting: findings from a replication," International Journal of Forecasting, Elsevier, vol. 16(1), pages 125-127.

   Cited by:

   1. Edward J. Lusk, 2019. "Time Series Forecasting in Stock Trading Markets: The Turning Point Curiosity," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 8(4), pages 01-16, July.
   2. Manuel Bern & Edward Lusk, 2020. "The Reduced Rules Rule Based Forecasting Decision Support System: Details and Functionalities: An Audit Context," Accounting and Finance Research, Sciedu Press, vol. 9(3), pages 1-13, August.
   3. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
   4. Adya, Monica & Collopy, Fred & Armstrong, J. Scott & Kennedy, Miles, 2001. "Automatic identification of time series features for rule-based forecasting," International Journal of Forecasting, Elsevier, vol. 17(2), pages 143-157.
   5. Frank Heilig & Edward J. Lusk, 2022. "A Conditioned Forecasting Model: A-priori Screening Validation Testing," International Business Research, Canadian Center of Science and Education, vol. 15(5), pages 1-63, May.
   6. Adya, Monica & Armstrong, J. Scott & Collopy, Fred & Kennedy, Miles, 2000. "An application of rule-based forecasting to a situation lacking domain knowledge," International Journal of Forecasting, Elsevier, vol. 16(4), pages 477-484.

4. Fred Collopy & Monica Adya & J. Scott Armstrong, 1994. "Research Report—Principles for Examining Predictive Validity: The Case of Information Systems Spending Forecasts," Information Systems Research, INFORMS, vol. 5(2), pages 170-179, June.

   Cited by:

   1. Jan Svanberg & Tohid Ardeshiri & Isak Samsten & Peter Öhman & Presha E. Neidermeyer & Tarek Rana & Natalia Semenova & Mats Danielson, 2022. "Corporate governance performance ratings with machine learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(1), pages 50-68, January.
   2. Korbinian Dress & Stefan Lessmann & Hans-Jorg von Mettenheim, 2017. "Residual Value Forecasting Using Asymmetric Cost Functions," Papers 1707.02736, arXiv.org.
   3. Robert G. Biscontri, 2012. "A Radial Basis Function Approach To Earnings Forecast," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(1), pages 1-18, January.

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