Data-Driven Hierarchical Forecasting Model for Point-of-Sale (POS) Transaction Counts | ||
| Advances in Industrial Engineering | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از 17 تیر 1405 | ||
| شناسه دیجیتال (DOI): 10.22059/aie.2026.413445.1980 | ||
| نویسندگان | ||
| S. Ali Torabi* ؛ Mahdieh Tavakoli | ||
| School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| Banks and other organizations in the financial sector, particularly payment service providers (PSPs), require accurate forecasts of transaction counts to optimize procurement planning for point-of-sale (POS) devices under budget constraints. To address this need, this study proposes a two-level hierarchical forecasting framework for POS transaction counts to support tactical decision-making. At the first level, a time-weighted Long Short-Term Memory (LSTM) model is developed to forecast monthly transaction counts over a one-year horizon. A set of candidate exogenous variables is initially identified and subsequently refined using Goal Programming Best-Worst Method (GP-BWM) to improve model performance. At the second level, a Random Forest (RF) model is employed to estimate the relative weights of daily transactions within each month based on intrinsic daily characteristics. A forward feature selection approach is further applied to identify the most influential daily features affecting transaction distribution. The proposed framework is validated through a real-world case study of a PSP in Iran. The results indicate that the time-weighted LSTM achieves high predictive accuracy with R² values of 0.90 and 0.89 on validation and test datasets for monthly forecasting, respectively. Additionally, the daily feature selection process demonstrates strong explanatory power with an R² of 0.9625. These findings confirm the effectiveness and practical applicability of the proposed framework in supporting data-driven planning. | ||
| کلیدواژهها | ||
| Hierarchical Forecasting؛ Long Short-Term Memory؛ Payment Service Providers؛ Point-of-Sale Transactions؛ Random Forest | ||
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آمار تعداد مشاهده مقاله: 185 |
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