ایزدی مهر، نرگس و جوانبخت، عذرا. (1392). استفاده از تئوری تعمیم یافته کالای مرکب برای گروه بندی برخی از محصولات زراعی در ایران طی دوره زمانی 88-74. همایش ملی پدافند غیر عامل در بخش کشاورزی، قشم.
امیرطاهری، محمد. (1399). پیشبینی چند متغیره سرعت باد در شبانه روز آینده با استفاده از شبکه عصبی عمیقLSTM . نهمین کنفرانس ملی شهرسازی ،معماری، عمران و محیط زیست. خراسان شمالی. موسسه پژوهشی رهجویان پایا. شیروان.
پیش بهار، اسماعیل؛ شهبازی، پریسا و حسین زاد، جواد. (1400). پیش بینی تولید، صادرات و واردات بخش کشاورزی ایران: ترکیب روش های اقتصادسنجی و پویایی های سیستم. فصلنامه اقتصاد کشاورزی، 15(1)، 28-1.
https://doi.org/10.22034/IAES.2021.526889.1829.
توحیدی، امیرحسین؛ زارع مهرجردی، محمدرضا؛ مهرابی بشرآبادی، حسین و نظام آبادی پور، حسین. (1394). ارزیابی مدل هیبرید شبکه عصبی مصنوعی-پانل دیتا در پیش بینی قیمت صادرات خشکبار ایران. فصلنامه اقتصاد مقداری، 12(3)، 95-116. https://doi.org/10.22055/jqe.2015.11895
حاج سید جوادی، سیدمحمدرضا و حیدری، رضا. (1401). طراحی مناسب ترین مدل هیبریدی پیش بینی قیمت آتی زعفران نگین در بورس کالای کشاورزی. مجله تحقیقات اقتصاد و توسعه کشاورزی ایران، 53(4)، 1041-1023.
حاجی زاده طحان، مرضیه؛ قاسم زاده، محمد و رضاییان، مهدی. (1399). یک شبکۀ حافظۀ طولانی کوتاه مدت عمیق مبتنی بر توجه تکاملی برای پیشبینی سری های زمانی. مجله هوش محاسباتی در مهندسی برق، 11(4)، 29-14.
خاکی، رویا. (1397). بررسی تاثیر عوامل داخلی بر میزان صادرات محصولات خشکبار(مطالعه موردی شرکت های صادرکننده استان خراسان و استان کرمان). پایان نامه کارشناسی ارشد گروه مدیریت. موسسه آموزش عالی غیرانتفاعی غیردولتی شاندیز.
خاتمی فیروزآبادی، سیدمحمدعلی؛ دهدشتی، شاهرخ؛ روشنی، افشان و اخگری، علی. (1395). اولویت بندی بازارهای هدف صادرات محصول پسته استان یزد با استفاده از مدل ترکیبی تحلیل سلسله مراتبی و تاپسیس در محیط فازی. پژوهشنامه بازرگانی، 20(79)، 154-121. https://doi.org/20.1001.1.17350794.1395.20.79.5.6
شاه آبادی، ابوالفضل و صفایی، مریم.(1396). تأثیر مؤلفه های دانش بر عرضه صادرات بخش کشاورزی ایران. پژوهشنامه بازرگانی، 21(84)، 26-1. https://doi.org/20.1001.1.17350794.1396.21.84.1.9
شاه ویردی، امیر؛ متفکرآزاد، محمدعلی؛ محمدزاده، پرویز و اسدزاده، احمد. (1399). شبیه سازی و پیش بینی صادرات اقتصاد ایران با بهره گیری از الگوریتم PSO در راستای دستیابی به اهداف سند چشم انداز بیست ساله. فصلنامه علمی مطالعات اقتصادی کاربردی ایران، 9(36)، 87-69. https://doi.org/10.22084/aes.2020.19379.2894
عدالت، محمدحسن؛ عزمی، رضا و باقری نژاد، جعفر. (1399). بهبود دقت پیشبینی فرآیندها در مدیریت فرآیندهای کسب وکار با بکارگیری معماری LSTM. مجله چشمانداز مدیریت صنعتی، 10(39)، 97-71. https://doi.org/10.52547/jimp.10.3.71
مهرابی بشرآبادی، حسین و کوچک زاده، سمیه. (1388). مدل سازی و پیش بینی صادرات محصولات کشاورزی ایران: کاربرد شبکه های عصبی مصنوعی. مجله اقتصاد و توسعه کشاورزی (علوم و صنایع کشاورزی)، 23(1)، 58-49.
نجفی، امیر و بیگی، جمال. (1398)، شناسایی عوامل موثر بر صادرات خشکبار در ایران. ششمین کنفرانس بین المللی حسابداری، مدیریت و نوآوری در کسب و کار، تهران.
نادرینیا، نادیا و رجبی، جهانگیر. (1400). شناسایی و رتبه بندی عوامل موثر بر صادرات خشکبار با استفاده از روش AHP. پژوهشهای کاربردی در مدیریت و حسابداری، 1(22)، 42-26. https://doi.org/10.22034/JOAS.2021.0812.0854
نجفی، بهاءالدین و طرازکار، محمد حسن. (1385). پیشبینی میزان صادرات پسته ایران، کاربرد شبکه عصبی مصنوعی. پژوهشنامه بازرگانی، 10(39)، 214-191.
هادی زاده، آرش؛ عباسی، جعفر و زروکی، شهریار. (1390). تحلیلی بر نظریه صادرات منجر به رشد در ایران. مطالعات و سیاست های اقتصادی، 17(84)، 59-74. https://doi.org/10.22096/esp.2011.26189
Adhikari, R., & Agrawal, R. K. (2014). A combination of artificial neural network and random walk models for financial time series forecasting. Neural Computing and Applications, 24(6), 1441–1449. https://doi.org/10.1007/s00521-013-1386-y.
Areekul, P., Senjyu, T., Toyama, H., & Yona, A. (2010). A hybrid ARIMA and neural network model for short-term price forecasting in deregulated market. IEEE Transactions on Power Systems, 25(1), 524–530. https://doi.org/10.1109/TPWRS.2009.2036488
Abbasimehr, H., & Khodizadeh Nahari, M. (2020). Improving demand forecasting with LSTM by taking into account the seasonality of data. Journal of Applied Research on Industrial Engineering, 7(2), 177–189. https://doi.org/10.22105/JARIE.2020.232594.1168
Akhter, M. R. (2021). LSTM-based deep learning model for stock prediction and predictive optimization model. EURO Journal on Decision Processes, 9(3), 1–11. https://doi.org/10.1016/j.procs.2021.12.011
Adalat, M. H., Azmi, R., & Bagherinejad, J. (2020). An enhanced LSTM method to improve the accuracy of the business process prediction. Industrial Management Perspective, 10(39), 71–97. https://doi.org/10.52547/JIMP.10.3.71. (In Persian)
Agarwal, S., Sharma, S., Faisal, K. N., & Sharma, R. R. (2024). Induced stress identification using EEG: A framework based on MVMD and machine learning. In 2024 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1–5). IEEE. https://doi.org/10.1109/sceecs61402.2024.10481868
Bengio, Y. (2013). Deep learning of representations: Looking forward. In Proceedings of the First International Conference on Statistical Language and Speech Processing (pp. 1–37). Springer. https://doi.org/10.1007/978-3-642-39593-2_1
Botunac, I., Bosna, J., & Matetic, M. (2024). Optimization of traditional stock market strategies using the LSTM hybrid approach. Information, 15(3), 1–17. https://doi.org/10.3390/info15030136
Bell, W. (2017). Foundations of futures studies: Human science for a new era. Transaction Publishers.
Bishop, P., Hines, A., & Collins, T. (2015). The current state of scenario development: An overview of techniques. Foresight, 17(1), 5–25. https://doi.org/10.1108/FS-04-2015-0026
Christopher, M., & Holweg, M. (2017). Supply chain 2.0: Managing supply chains in the era of turbulence. International Journal of Physical Distribution & Logistics Management, 47(1), 2–16. https://doi.org/10.1108/IJPDLM-09-2016-0282
Cunha, M. P., Simpson, M., & Clausen, J. (2020). Circular economy and sustainability: A review of current trends. Journal of Cleaner Production, 251, 118498. https://doi.org/10.1016/j.jclepro.2019.118498
Chen, J., Chen, H., Hao, Y., & Gao, W. (2017). Application of SVR models in stock index forecast based on different parameter search methods. Open Journal of Statistics, 7(2), 194–202. https://doi.org/10.4236/ojs.2017.72015
Dias, F., Lourenço, N., & Rua, A. (2018). Forecasting exports with targeted predictors. Banco de Portugal Economic Studies, 4(2), 45–62.
Fischer, T., & Krauss, C. (2017). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. https://doi.org/10.1016/j.ejor.2017.11.054
Food and Agriculture Organization of the United Nations (FAO). (2021). Strengthening agricultural trade in geopolitical contexts. Rome: FAO.
Freeman, R. E., Harrison, J. S., Wicks, A. C., Parmar, B. L., & de Colle, S. (2010). Stakeholder theory: The state of the art. Cambridge University Press.
Food and Agriculture Organization of the United Nations (FAO). (2020). Agricultural and food classification. Retrieved from https://www.fao.org
Hope, D. (2021). Economic nowcasting with long short-term memory artificial neural networks (LSTM). Journal of Statistics, 58(3), 211–230. https://doi.org/10.2478/jos-2022-0037
Hollensen, S. (2015). Global marketing: A decision-oriented approach (6th ed.). Pearson Education.
Haj Seyed Javadi, S. M. R., & Heydari, R. (2022). Designing the most suitable hybrid model for forecasting the future price of saffron in the agricultural commodity exchange. Iranian Journal of Agricultural Economics and Development Research, 53(4), 1023–1041. https://doi.org/10.22059/IJAEDR.2022.336850.669122. (In Persian)
Hadizadeh, A., Abbasi, J., & Zarouki, Sh. (2011). An analysis of the export-led growth hypothesis in Iran. Economic Studies and Policies (Biannual Journal), 17(84), 59–74. https://doi.org/10.22096/esp.2011.26189. (In Persian)
Hajizadeh Tahan, M., Ghasemzadeh, M., & Rezaeian, M. (2020). An evolutionary attention-based deep long short-term memory for time series prediction. Journal of Computational Intelligence in Electrical Engineering, 11(4), 14–29. https://doi.org/10.22108/ISEE.2020.121597.1334(In Persian)
International Nut and Dried Fruit Council (INC). (2022). Annual report on dried fruits and nuts classification. Retrieved from https://www.nutfruit.org
Hanusch, F. (2019). Digital marketing strategies in international markets. Journal of International Marketing, 27(2), 45–60. https://doi.org/10.1057/s41267-019-00114-9
Inayatullah, S. (2008). Questioning the future: Methods and tools for organisational and societal transformation. Tamkang University Press.
International Fund for Agricultural Development (IFAD). (2020). The role of nuts and dried fruits in global agriculture. Rome: IFAD.
Izadi-Mehr, N., & Javan-Bakht, A. (2013). Application of the generalized composite commodity theorem for grouping some agricultural products in Iran during the period 1995–2009. National Conference on Passive Defense in Agriculture, Qeshm, Iran. (In Persian)
Johnson, M. P., & Schaltegger, S. (2016). Sustainability marketing: A global perspective. Journal of Business Ethics, 133(1), 1–12. https://doi.org/10.1007/s10551-014-2443-y
Jungsik, H. (2020). Modeling financial time series using LSTM with trainable initial hidden states. Cornell University Journal, 14(1), 1–5. https://doi.org/10.48550/arXiv.2007.06848
Keller, K. L., & Richey, F. J. (2006). The importance of corporate brand management in global markets. Journal of International Marketing, 14(4), 1–25. https://doi.org/10.1509/jimk.14.4.1
Quach, K. N. D., Ren, Z., Tran, K. V., Vu, V. H., Chun, Y., & Nguyen, T. T. (2023). Short-term traffic speed prediction using hybrid LSTM-SVR model. In Proceedings of the International Conference on Robot Intelligence Technology and Applications (pp. 1–10). Springer. https://doi.org/10.1007/978-3-031-26889-2_40
Kviesis, A., Zacepins, A., Komasilovs, V., & Munizaga, M. (2018). Bus arrival time prediction with limited data set using regression models. In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS) (pp. 643–647). SciTePress. https://doi.org/10.5220/0006816306430647
Khaki, R. (2018). Examining the impact of internal factors on the level of dried fruit exports: A case study of exporting companies in Khorasan and Kerman provinces (Master’s thesis). Department of Management, Shandiz Non-Profit Institute of Higher Education. (In Persian)
Khatami Firouzabadi, S. M. A., Dehdashti Shahrokh, Z., Roshani, A., & Akhgari, A. (2016). A hybrid fuzzy approach using AHP and TOPSIS methods to prioritize the export target markets of pistachio in Iran, Yazd. Journal of Trade Studies, 20(79), 121–154. https://doi.org/20.1001.1.17350794.1395.20.79.5.6. (In Persian)
Luo, J., Hong, T., Gao, Z., & Fang, C. (2023). A robust support vector regression model for electric load forecasting. International Journal of Forecasting, 39(2), 1005–1020. https://doi.org/10.1016/j.ijforecast.2022.04.001
Lewbel, A. (1996). Aggregation without separability: A generalized composite commodity theorem. American Economic Review, 86(3), 524–561. https://www.jstor.org/stable/2118210
Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2018). Stock price prediction using support vector regression on daily and up to the minute prices. The Journal of Finance and Data Science, 4(3), 183–201. https://doi.org/10.1016/j.jfds.2018.04.003
Moghar, A., & Hamiche, M. (2020). Stock market prediction using LSTM recurrent neural network. Procedia Computer Science, 170, 1168–1173. https://doi.org/10.1016/j.procs.2020.03.049
Mehrabi Boshrabadi, H., & Koochakzadeh, S. (2009). Modeling and forecasting of agricultural products export in Iran: Application of artificial neural networks. Journal of Economics and Agricultural Development, 23(1), 49–58. https://doi.org/10.22067/jead2.v1388i1.2067. (In Persian)
Naderinia, N., & Rajabi, J. (2021). Identifying and ranking the factors affecting dried fruit exports using the AHP method. Applied Research in Management and Accounting, 1(22), 26–42. https://doi.org/10.22034/JOAS.2021.0812.0854. (In Persian)
Najafi, A., & Beigi, J. (2019). Identifying the factors affecting dried fruit exports in Iran. 6th International Conference on Accounting, Management, and Business Innovation, Tehran, Iran. (In Persian)
Najafi, B. A., & Tarazkar, M. H. (2006). Forecasting exports of pistachio from Iran: Application of artificial neural network. Journal of Trade Studies, 10(39), 191–214. (In Persian)
OECD. (2022). The role of AI in economic forecasting and trade. Organization for Economic Co-operation and Development.
Porter, M. E. (1998). The competitive advantage of nations. Free Press.
Pishbahar, E., Shahbazi, P., & Hosseinzad, J. (2021). Prediction of production, export, and import of Iran’s agricultural sector: The combination of econometrics and system dynamics methods. Agricultural Economics, 15(1), 1–28.https://doi.org/10.22034/IAES.2021.526889.1829. (In Persian)
Ruiz-Aguilar, J. J., Turias, I. J., & Jiménez-Come, M. J. (2014). Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting. Transportation Research Part E: Logistics and Transportation Review, 67, 1–13. https://doi.org/10.1016/j.tre.2014.03.005
Rameha, T., Abbasib, R., & Sanaei, M. (2021). Designing a hybrid model for stock market prediction based on LSTM and transfer learning. International Journal of Nonlinear Analysis and Applications, 12, 2325–2337. https://doi.org/10.22075/ijnaa.2021.6250
Ricardo, D. (2015). On the principles of political economy and taxation. Cambridge University Press. https://doi.org/10.1017/CBO9781107589421
Slaughter, R. A. (2015). The foresight principle: Cultural recovery in the 21st century. Routledge.
Sakshi, K., & Vijayalakshmi, A. (2020). An ARIMA-LSTM hybrid model for stock market prediction using live data. Journal of Engineering Science and Technology Review, 13(4), 117–123. https://doi.org/10.25103/jestr.134.11
Shen, M. L., Lee, C. F., Liu, H. H., Chang, P. Y., & Yang, C. H. (2021). Effective multinational trade forecasting using LSTM recurrent neural network. Expert Systems with Applications, 182, 115199. https://doi.org/10.1016/j.eswa.2021.115199
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Sohrabpour, V., Oghazi, P., Toorajipour, R., & Nazarpour, A. (2021). Export sales forecasting using artificial intelligence. Technological Forecasting and Social Change, 163, 120480. https://doi.org/10.1016/j.techfore.2020.120480
Sakarya, S., Eckman, M., & Hyllegard, K. H. (2007). Market selection for international expansion. International Marketing Review, 24(2), 208–238. https://doi.org/10.1108/02651330710741820
Shen, J., Chen, H., Wang, L., & Jin, Y. (2021). Towards a more effective bidirectional LSTM-based learning model for human-bacterium protein-protein interactions. In Advances in Intelligent Systems and Computing (pp. 1–10). Springer. https://doi.org/10.1007/978-3-030-54568-0_10
Shahabadi, A., & Safaee, M. (2017). Effects of knowledge factors on supply of agricultural exports of Iran. Journal of Trade Studies, 21(84), 1–26. https://doi.org/20.1001.1.17350794.1396.21.84.1.9. (In Persian)
Shahvirdi, A., Motafakker Azaad, M. A., Mohammadzadeh, P., & Asadzadeh, A. (2021). Simulation and prediction of Iran's exports using the PSO algorithm to achieve the objectives of the twenty years vision document: A case study—Comparison with Turkish economy. Applied Economics Studies, Iran (AESI), 9(36), 69–87. https://doi.org/10.22084/AES.2020.19379.2894. (In Persian)
Tohidi, A. H., Zare Mehrjerdi, M. R., Mehrabi Boshrabadi, H., & Nezamabadi-Pour, H. (2015). Evaluation of artificial neural network–panel data hybrid model in predicting Iran’s dried fruits export prices. Quarterly Journal of Quantitative Economics (JQE), 12(3), 95–116. https://doi.org/10.22055/jqe.2015.11895. (In Persian)
Taheri, M. (2020). Multivariate forecasting of wind speed for the next 24 hours using deep LSTM neural networks. In 9th National Conference on Urban Planning, Architecture, Civil Engineering, and Environment (Shirvan, North Khorasan, Iran). Rahjuyan Paya Research Institute. (In Persian)
UNCTAD. (2019). The role of economic diplomacy in agricultural trade. United Nations Conference on Trade and Development.
Vapnik, V. N. (1995). The nature of statistical learning theory. Springer.
World Economic Forum. (2023). The future of global trade: Trends and strategies. World Economic Forum.
Wang, H., Liang, T., & Cheng, Y. (2021). Prediction of perceived utility of consumer online reviews based on LSTM neural network. Mobile Information Systems, 2021, Article 5482662, 1–7. https://doi.org/10.1155/2021/5482662
World Bank. (2019). Economic diplomacy and sanction mitigation strategies. World Bank Group.
Wren-Lewis, S. (1997). Conflicts between macroeconomic forecasting and policy analysis. In Empirical Models and Policy Making Conference, Amsterdam. https://doi.org/10.4324/9780203130490-38
Xie, J., Lin, Z., Yin, J., Lai, Z., Wang, X., & Chen, X. (2024). Deep reinforcement learning-based dynamic bus timetable scheduling with bidirectional constraints. In Proceedings of the 91st International Conference on Big Data and Social Computing (BDSC) (pp. 1–10). Springer. https://doi.org/10.1007/978-981-97-5803-6_8
Zhili, G., Yang, L., Li, J., Chen, Y., & Xu, Y. (2024). Bus schedule time prediction based on LSTM-SVR model. Mathematics, 12(12), 1–15. https://doi.org/10.3390/math12223589
Zhavoronkov, A., Mamoshina, P., & Vanhaelen, Q. (2019). Deep learning for aging and longevity research. Frontiers in Genetics, 10, 610. https://doi.org/10.3389/fgene.2019.00610
