Deep Learning Models for Price Prediction in Travertine Stone Mines: A Comparison of LSTM, Transformer, and Hybrid Models

سال انتشار: 1404
محل انتشار: یازدهمین کنفرانس بین المللی مهندسی صنایع و سیستم ها
کد COI اختصاصی: ICISE11_019
زبان مقاله: انگلیسی
تعداد مشاهده: 49

نویسندگان

Department of Industrial Engineering, Qom University of Technology, Qom, Iran

Department of Statistics, Mathematics, and Computer Science, Allameh Tabataba'i University, Tehran, Iran

چکیده

< p> This study aims to develop and evaluate four deep learning models for forecasting the monthly prices offered by stone suppliers in Iran. Given the simultaneous influence of temporal factors (such as exchange rate, inflation, fuel price, and order volume) and static attributes (including block quality, brand reputation, and cooperation history), each model was designed with a dual-input structure to separately process sequential and non-sequential features, which are then integrated at a later stage. The implemented architectures include LSTM, Bi-LSTM, Transformer, and a hybrid Transformer+LSTM+CLS model. The models were trained and evaluated using data collected from five different mines over several months, ensuring robustness and generalizability across diverse supply sources and time periods. Model performance was assessed using key evaluation metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), the coefficient of determination (R&sup۲;), and validation loss. The results indicate that the Transformer model achieved the highest accuracy, with the lowest prediction errors and the best generalization capability. The hybrid model also performed comparably well, making it a robust alternative for more complex forecasting tasks. In contrast, the Bi-LSTM model underperformed and is less recommended for this application. Overall, the findings highlight that combining attention-based architectures with sequential analysis provides an effective solution for price forecasting in data-driven and competitive industrial contexts.< /p>

کلیدواژه ها

Price Forecasting, Deep Learning Models, Transformer Architecture, Time Series Forecasting, Stone Suppliers, Data-driven

مقالات مرتبط جدید

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.