Forecasting Iran’s Saffron Export by Comparison of Machine Learning Algorithms

محورهای موضوعی : Decision-making

علیرضا امیرتیموری ¹ (استاد تمام، گروه ریاضی، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران.)
منصور صوفی ² (استادیار، گروه مدیریت صنعتی، واحدرشت، دانشگاه آزاداسلامی، رشت، ایران)
مهدی همایونفر ³ (استادیار، گروه مدیریت صنعتی، واحدرشت، دانشگاه آزاداسلامی، رشت، ایران)
مهدی فدایی ⁴ (استادیار، گروه مدیریت صنعتی، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران)

تاریخ دریافت : 1399/02/01 تاریخ پذیرش : 1400/11/24 تاریخ انتشار : 1402/03/11

کلید واژه: Neural network, Prediction, deep learning, Saffron Exports, Gradient Boost Tree,

چکیده مقاله :

Imports and exports play an integral role in the economic growth of all countries. Therefore, selecting the right products can enhance a country's competitiveness in global trade. Saffron stands out as one of Iran's most vital and unique non-oil products for export. The objective of this study was to predict saffron exports using three data mining algorithms and determine the most suitable algorithm for forecasting. The sample period for the forecasting models encompasses saffron export data from Iran for the years 2012 to 2019, gathered from the Iran Saffron Association. Following the data preparation steps, saffron export was forecasted using three data mining algorithms: artificial neural network, deep learning, and gradient boost tree. The validity of the models plays a crucial role in selecting the best forecasting model. The predictive validity of the three designed models was evaluated using the absolute error (artificial neural network = 0.036, deep learning network = 0.031, and gradient boost tree = 0.047), R-squared (artificial neural network = 0.045, deep learning network = 0.044, and gradient boost tree = 0.073), and correlation coefficients (artificial neural network = 0.95, deep learning network = 0.98, and gradient boost tree = 0.97). Based on the findings, all models demonstrate high accuracy, with very low prediction errors that are closely matched. However, the deep learning network exhibits a slightly lower, albeit statistically insignificant, error. These results can be valuable for enhancing the precision of saffron export planning.

چکیده انگلیسی:

واردات و صادرات در همه کشورها نقش مهمی در رشد اقتصادی ایفا می‌کنند. بنابراین، انتخاب محصولات مناسب، باعث افزایش رقابت‌پذیری کشور در تجارت جهانی می‌شود. زعفران یکی از مهم‌ترین و متمایزترین محصولات غیرنفتی ایران برای صادرات است. هدف این مطالعه، پیش‌بینی صادرات زعفران از طریق سه الگوریتم داده‌کاوی و انتخاب الگوریتم مناسب در پیش‌بینی است. دوره نمونه مدل‌های پیش‌بینی شامل داده‌های صادرات زعفران ایران از سال ۲۰۱۲ تا ۲۰۱۹ است که از انجمن زعفران ایران جمع‌آوری شده‌اند. پس از انجام مراحل آماده‌سازی داده، پیش‌بینی صادرات زعفران با استفاده از سه الگوریتم داده‌کاوی شامل شبکه عصبی مصنوعی، یادگیری عمیق و درخت تقویت گرادیانی انجام شد. برای انتخاب یک مدل پیش‌بینی بهتر، اعتبار مدل نقش مهمی ایفا می‌کند. صحت پیش‌بینی سه مدل طراحی شده به کمک خطای مطلق ( 036/0 = شبکه‌ی عصبی مصنوعی،  031/0 = یادگیری عمیق شبکه،   047/0 = درخت تقویت گرادیانی)، معیار R2 (045/0 = شبکه‌ی عصبی مصنوعی، 044/0 = یادگیری عمیق شبکه، 073/0 = درخت تقویت گرادیانی) و همبستگی (95/0 = شبکه‌ی عصبی مصنوعی، 98/0 = یادگیری عمیق شبکه،  97/0 = درخت تقویت گرادیانی) اندازه‌گیری شدند. براساس یافته‌ها، تمامی این سه مدل طراحی شده دقیق هستند و خطای پیش‌بینی آن‌ها بسیار کم و نزدیک به هم است. اما با تفاوت ناچیز، شبکه یادگیری عمیق کمترین خطا را دارد. نتایج می‌توانند برای برنامه‌ریزی دقیق‌تر صادرات زعفران مفید باشند.

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