Hierarchical container throughput forecasting: The value of coherent forecasts in the management of ports operations. (November 2022)
- Record Type:
- Journal Article
- Title:
- Hierarchical container throughput forecasting: The value of coherent forecasts in the management of ports operations. (November 2022)
- Main Title:
- Hierarchical container throughput forecasting: The value of coherent forecasts in the management of ports operations
- Authors:
- Sanguri, Kamal
Shankar, Sonali
Punia, Sushil
Patra, Sabyasachi - Abstract:
- Highlights: Addresses the problem of incoherency in container throughput forecasting. Proposes a cross-temporal forecasting framework for container throughput. Provides coherent forecasts at all levels and hierarchical dimensions. Used Port of Los Angeles as the test case. Benchmarked different reconciliation weights approximation techniques. Abstract: A port's overall container throughput (CT) data can be arranged into diverse hierarchical dimensions (cross-sectional or temporal). Wherein the forecasts are generated using the " horses-for-courses " approach to achieve maximum accuracy at the base level. Generally, these base forecasts do not add up across temporal or cross-sectional hierarchies and give rise to the issue of forecast " incoherence ". Accordingly, the present study extends the idea of utilising optimal forecast reconciliation techniques in CT forecasting. However, the frameworks of these techniques do not allow for their interaction across different dimensions of hierarchy present in CT data. Hence, the paper addresses this problem by proposing a cross-temporal forecast reconciliation framework. Further, monthly CT data from the Port of Los Angeles is used for empirical analysis. It is observed that the proposed framework, with exponential smoothing as the base forecasting method, can provide coherent forecasts at all levels and hierarchical dimensions while simultaneously improving the accuracy of the forecasts. Further, the study also devises a mechanism toHighlights: Addresses the problem of incoherency in container throughput forecasting. Proposes a cross-temporal forecasting framework for container throughput. Provides coherent forecasts at all levels and hierarchical dimensions. Used Port of Los Angeles as the test case. Benchmarked different reconciliation weights approximation techniques. Abstract: A port's overall container throughput (CT) data can be arranged into diverse hierarchical dimensions (cross-sectional or temporal). Wherein the forecasts are generated using the " horses-for-courses " approach to achieve maximum accuracy at the base level. Generally, these base forecasts do not add up across temporal or cross-sectional hierarchies and give rise to the issue of forecast " incoherence ". Accordingly, the present study extends the idea of utilising optimal forecast reconciliation techniques in CT forecasting. However, the frameworks of these techniques do not allow for their interaction across different dimensions of hierarchy present in CT data. Hence, the paper addresses this problem by proposing a cross-temporal forecast reconciliation framework. Further, monthly CT data from the Port of Los Angeles is used for empirical analysis. It is observed that the proposed framework, with exponential smoothing as the base forecasting method, can provide coherent forecasts at all levels and hierarchical dimensions while simultaneously improving the accuracy of the forecasts. Further, the study also devises a mechanism to evaluate the efficacy of the proposed framework in providing decision support for various port operations (the requirement for terminal ground slots and gantry cranes). The results indicate that improvements could be made in port operations decisions using the proposed framework. … (more)
- Is Part Of:
- Computers & industrial engineering. Volume 173(2022)
- Journal:
- Computers & industrial engineering
- Issue:
- Volume 173(2022)
- Issue Display:
- Volume 173, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 173
- Issue:
- 2022
- Issue Sort Value:
- 2022-0173-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-11
- Subjects:
- Forecasting -- Container supply chain -- Hierarchical forecasting -- Temporal reconciliation -- Cross-temporal forecasting
Engineering -- Data processing -- Periodicals
Industrial engineering -- Periodicals
620.00285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03608352 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.cie.2022.108651 ↗
- Languages:
- English
- ISSNs:
- 0360-8352
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.713000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 24154.xml