معرفی یک سیستم پیش‌بینی مناسب برای برآورد تقاضای درمان در بیمارستان امام رضا(ع) ارومیه

آقایی، کیومرث و پورمیری، بهروز (1385)؛ پیش­بینی روند قیمت فولاد با استفاده از شبکه­های عصبی مصنوعی و مقایسه نتایج آن با روش آریما، فصلنامه بررسی­های اقتصادی، دوره 3، شماره1: 4-5.

آیین‌نامه نحوه تأسیس و اداره بیمارستان‌ها، شماره نامه 5234، تاریخ 21/5/1376.

ابریشمی، حمید؛ جبل عاملی، فرخنده؛ ابوالحسنی، معصومه و جوان، افشین (1393)؛ عملکرد دو روش ARIMA و شبکه عصبی GMDH در پیش بینی تقاضای گاز طبیعی در بخش‌های مختلف (ایران-1380-1389)، فصلنامه مطالعات اقتصادی کاربردی ایران، سال سوم، شماره 12: 33-57.

جعفرنژاد، احمد و محسن سلیمانی (1390)؛ پیش­بینی تقاضای تجهیزات پزشکی بر اساس شبکه­های عصبی مصنوعی و روش ARIMA، فصلنامه پژوهش­ها و سیاست­های اقتصادی، سال نوزدهم، شماره57: 3-4.

فیاض­بخش، احمد (1389)؛ بررسی آگاهی و نگرش مدیران ارشد و میانی در امکان استفاده از مدیریت کیفیت فراگیر در بیمارستان، مجله تحقیقات سلامت، شماره 3: 5-8.

قاسمی، عبدالرسول؛ اسد پور، حسن و شاصادقی، مختار (1379)؛ کاربرد شبکه عصبی در پیش­بینی سری­های زمانی و مقایسه آن با مدل آریما، پژوهشنامه بازرگانی، 120: 14-87.

گزارش نهایی چهل و هشتمین اجلاس رؤسای دانشگاه‌ها، دانشکده‌های علوم پزشکی و خدمات بهداشتی درمانی کشور. سیمای فرهنگ، 1382.

Abraham, B. and Ledolter, J. (1986); Forecast functions implied by autoregressive integrated moving average models and other related forecast procedures. International Statistical Review/ Revue Internationale de Statistique, 51-66.

Aburto, L. and Weber, R. (2007); Improved supply chain management based on hybrid demand forecasts. Applied Soft Computing, 7(1), 136-144.

Armstrong, J. S. (2001); Principles of forecasting: a handbook for researchers and practitioners (Vol. 30): Springer Science & Business Media.

Atiya, A. F. (2001); Bankruptcy prediction for credit risk using neural networks: A survey and new results. Neural Networks, IEEE Transactions on, 12(4), 929-935.

Bosarge, W. (1993); Adaptive processes to exploit the nonlinear structure of financial markets. Neural Networks in Finance and Investing. Probes Publishing, 371-402.

Fausett, L. V., & Hall, P. (1994); Fundamentals of neural networks: architectures, algorithms, and applications (Vol. 40): Prentice-Hall Englewood Cliffs.

Flores, J. J., Graff, M., & Rodriguez, H. (2012); Evolutive design of ARMA and ANN models for time series forecasting. Renewable Energy, 44, 225-230.

Garcia, K. A. (2011); Using a Randomized Regression Approach to Estimate Hospital Admissions to Reduce Emergency Department Holding. Citeseer.  

Hæke, C. and Helmenstein, C. (1996); Neural networks in the capital markets: An application to index forecasting. Computational Economics, 9(1), 37-50.

Hill, T.; Marquez, L.; O'Connor, M. and Remus, W. (1994); Artificial neural network models for forecasting and decision making. International Journal of Forecasting, 10(1), 5-15.

Hobbs, B. F., Helman, U., Jitprapaikulsarn, S., Konda, S. and Maratukulam, D. (1998); Artificial neural networks for short-term energy forecasting: Accuracy and economic value. Neurocomputing, 23(1), 71-84.

Huang, W.; Lai, K. K.; Nakamori, Y. and Wang, S. (2004); Forecasting foreign exchange rates with artificial neural networks: a review. International Journal of Information Technology & Decision Making, 3(01), 145-165.

Isaaks, E. H. and Srivastava, R. M. (1989); An introduction to applied geostatistics.

Kohzadi, N., Boyd, M. S., Kaastra, I., Kermanshahi, B. S. and Scuse, D. (1995); Neural networks for forecasting: an introduction. Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, 43(3), 463-474.

Kuan, C. M., and White, H. (1994); Artificial neural networks: an econometric perspective∗. Econometric Reviews, 13(1), 1-91.

Kuo, R. (2001); A sales forecasting system based on fuzzy neural network with initial weights generated by genetic algorithm. European Journal of Operational Research, 129(3), 496-517.

Kuo, R. J.; Wu, P. and Wang, C. (2002); An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination. Neural networks, 15(7), 909-925.

Law, R. and Au, N. (1999); A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management, 20(1), 89-97.

Ledolter, J. (1989); The effect of additive outliers on the forecasts from ARIMA models. International Journal of Forecasting, 5(2), 231-240.

Lee, T. H.; White, H. and Granger, C. W. (1993); Testing for neglected nonlinearity in time series models: A comparison of neural network methods and alternative tests. Journal of Econometrics, 56(3), 269-290.

Leegon, J.; Jones, I.; Lanaghan, K. and Aronsky, D. (2006); Predicting hospital admission in a pediatric emergency department using an artificial neural network. Paper presented at the AMIA Annual Symposium Proceedings.

Li, J.; Guo, L. and Handly, N. (2009); Hospital admission prediction using pre-hospital variables. Paper presented at the Bioinformatics and Biomedicine, 2009. BIBM'09. IEEE International Conference on.

Liu, Y.; Wang, D. and Ding, F. (2010); Least squares based iterative algorithms for identifying Box–Jenkins models with finite measurement data. Digital Signal Processing, 20(5), 1458-1467.

Moshiri, S. and Cameron, N. E. (1999); Neural network versus econometric models in forecasting inflation. Journal of forecasting, 19.

Moshiri, S.; Cameron, N. E. and Scuse, D. (1999); Static, dynamic, and hybrid neural networks in forecasting inflation. Computational Economics, 14(3), 219-235.

Nelson, C. R. and Plosser, C. R. (1982); Trends and random walks in macroeconmic time series: some evidence and implications. Journal of monetary economics, 10(2), 139-162.

Palmer, A.; Montano, J. J. and Sese, A. (2006); Designing an artificial neural network for forecasting tourism time series. Tourism Management, 27(5), 781-790.

Porter, M. and Stern, S. (2001); Location matters. Sloan Management Review, 42(4), 28-36.

Reddy, T. A. (2011); Applied data analysis and modeling for energy engineers and scientists: Springer Science & Business Media.

Sermpinis, G.; Dunis, C.; Laws, J. and Stasinakis, C. (2012); Forecasting and trading the EUR/USD exchange rate with stochastic Neural Network combination and time-varying leverage. Decision Support Systems, 54(1), 316-329.

Sözen, A.; Arcaklioğlu, E. and Özkaymak, M. (2005); Turkey’s net energy consumption. Applied Energy, 81(2), 209-221.

Tang, Z.; de Almeida, C. and Fishwick, P. A. (1991); Time series forecasting using neural networks vs. Box-Jenkins methodology. Simulation, 57(5), 303-310.

Trippi, R. R. and Turban, E. (1992); Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real World Performance: McGraw-Hill, Inc.

Valipour, M.; Banihabib, M. E. and Behbahani, S. M. R. (2013); Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly in flow of Dez dam reservoir. Journal of Hydrology, 476, 433-441.

White, H. (1988); Economic prediction using neural networks: The case of IBM daily stock returns. Paper presented at the Neural Networks, 1988, IEEE International Conference on.

“MATLAB Tutorial”, http://www.mathworks.com.