Leveraging Large Language Models for Discrepancy Value Prediction in Custody Transfer Systems: A Comparative Analysis of Probabilistic and Point Forecasting Approaches
Discrepancies in custody transfer systems in the oil and gas industry pose significant financial, regulatory, and operational risks. Accurate prediction of these discrepancies is critical to optimizing operations and minimizing potential losses. This study evaluates the effectiveness of Large Langua...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10964215/ |
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| author | Fiki Hidayat Arbi Haza Nasution Fajril Ambia Dike Fitriansyah Putra Mulyandri |
| author_facet | Fiki Hidayat Arbi Haza Nasution Fajril Ambia Dike Fitriansyah Putra Mulyandri |
| author_sort | Fiki Hidayat |
| collection | DOAJ |
| description | Discrepancies in custody transfer systems in the oil and gas industry pose significant financial, regulatory, and operational risks. Accurate prediction of these discrepancies is critical to optimizing operations and minimizing potential losses. This study evaluates the effectiveness of Large Language Models (LLMs), specifically the Chronos-FineTuning Amazon Chronos T5 Small model, alongside statistical, machine learning, and deep learning models, in both probabilistic and point forecasting tasks. The evaluation covers metrics such as Weighted Quantile Loss (WQL), Scaled Quantile Loss (SQL), Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (SMAPE), and Root Mean Square Error (RMSE). The results highlight the superior performance of the Chronos model in both forecasting paradigms, demonstrating its ability to capture uncertainty and deliver precise predictions. This research offers valuable insights into selecting forecasting methodologies to improve custody transfer operations, underscoring the transformative potential of LLMs in industrial applications. |
| format | Article |
| id | doaj-art-e16f99061ed3415a96f75a63b731ba09 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e16f99061ed3415a96f75a63b731ba092025-08-20T02:27:16ZengIEEEIEEE Access2169-35362025-01-0113656436565810.1109/ACCESS.2025.356025410964215Leveraging Large Language Models for Discrepancy Value Prediction in Custody Transfer Systems: A Comparative Analysis of Probabilistic and Point Forecasting ApproachesFiki Hidayat0https://orcid.org/0000-0003-1407-8952Arbi Haza Nasution1https://orcid.org/0000-0001-6283-3217Fajril Ambia2https://orcid.org/0000-0002-3905-4300Dike Fitriansyah Putra3https://orcid.org/0000-0002-6772-130X Mulyandri4Department of Petroleum Engineering, Universitas Islam Riau, Pekanbaru, Riau, IndonesiaDepartment of Informatics Engineering, Universitas Islam Riau, Pekanbaru, Riau, IndonesiaDepartment of Petroleum Engineering, Universitas Islam Riau, Pekanbaru, Riau, IndonesiaDepartment of Petroleum Engineering, Universitas Islam Riau, Pekanbaru, Riau, IndonesiaPT Pertamina Hulu Rokan, Pekanbaru, Riau, IndonesiaDiscrepancies in custody transfer systems in the oil and gas industry pose significant financial, regulatory, and operational risks. Accurate prediction of these discrepancies is critical to optimizing operations and minimizing potential losses. This study evaluates the effectiveness of Large Language Models (LLMs), specifically the Chronos-FineTuning Amazon Chronos T5 Small model, alongside statistical, machine learning, and deep learning models, in both probabilistic and point forecasting tasks. The evaluation covers metrics such as Weighted Quantile Loss (WQL), Scaled Quantile Loss (SQL), Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (SMAPE), and Root Mean Square Error (RMSE). The results highlight the superior performance of the Chronos model in both forecasting paradigms, demonstrating its ability to capture uncertainty and deliver precise predictions. This research offers valuable insights into selecting forecasting methodologies to improve custody transfer operations, underscoring the transformative potential of LLMs in industrial applications.https://ieeexplore.ieee.org/document/10964215/Probabilistic time-series forecastinglarge language modelsdiscrepancycustody transfer system |
| spellingShingle | Fiki Hidayat Arbi Haza Nasution Fajril Ambia Dike Fitriansyah Putra Mulyandri Leveraging Large Language Models for Discrepancy Value Prediction in Custody Transfer Systems: A Comparative Analysis of Probabilistic and Point Forecasting Approaches IEEE Access Probabilistic time-series forecasting large language models discrepancy custody transfer system |
| title | Leveraging Large Language Models for Discrepancy Value Prediction in Custody Transfer Systems: A Comparative Analysis of Probabilistic and Point Forecasting Approaches |
| title_full | Leveraging Large Language Models for Discrepancy Value Prediction in Custody Transfer Systems: A Comparative Analysis of Probabilistic and Point Forecasting Approaches |
| title_fullStr | Leveraging Large Language Models for Discrepancy Value Prediction in Custody Transfer Systems: A Comparative Analysis of Probabilistic and Point Forecasting Approaches |
| title_full_unstemmed | Leveraging Large Language Models for Discrepancy Value Prediction in Custody Transfer Systems: A Comparative Analysis of Probabilistic and Point Forecasting Approaches |
| title_short | Leveraging Large Language Models for Discrepancy Value Prediction in Custody Transfer Systems: A Comparative Analysis of Probabilistic and Point Forecasting Approaches |
| title_sort | leveraging large language models for discrepancy value prediction in custody transfer systems a comparative analysis of probabilistic and point forecasting approaches |
| topic | Probabilistic time-series forecasting large language models discrepancy custody transfer system |
| url | https://ieeexplore.ieee.org/document/10964215/ |
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