Shipping market forecasting by forecast combination mechanism
The volatile characteristics of the tanker market pose challenges to forecasting. In addition, the volatile characteristics of newbuilding and secondhand ship prices, time charter rates, and scrap values make developing a unified framework of forecasting difficult. Most researchers have developed fo...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/160315 |
| _version_ | 1826113295188230144 |
|---|---|
| author | Gao, Ruobin Liu, Jiahui Du, Liang Yuen, Kum Fai |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Gao, Ruobin Liu, Jiahui Du, Liang Yuen, Kum Fai |
| author_sort | Gao, Ruobin |
| collection | NTU |
| description | The volatile characteristics of the tanker market pose challenges to forecasting. In addition, the volatile characteristics of newbuilding and secondhand ship prices, time charter rates, and scrap values make developing a unified framework of forecasting difficult. Most researchers have developed forecasting models and evaluated their performance based on a specific market. Such narrow development imposes difficulty for practitioners to choose a suitable model. Due to the boom of machine learning, many researchers are trying to boost the forecasting accuracy of shipping markets using machine learning. However, there are many hyper-parameters of the complex machine learning models and a slight variation of the model may cause significant performance degradation. This paper utilizes a forecast combination mechanism to forecast many time series collected from the shipping market, including newbuilding and secondhand ship prices, scrap values, and time charter rates. The models inside the combination pool are just linear functions. Finally, we compare their performance with conventional machine learning models and naïve forecasts using three error metrics and statistical tests. The statistical tests show that the combination of linear models is superior. The findings of this study also indicate that complex models do not boost forecasting accuracy necessarily. |
| first_indexed | 2024-10-01T03:20:52Z |
| format | Journal Article |
| id | ntu-10356/160315 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:20:52Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1603152022-07-19T04:32:09Z Shipping market forecasting by forecast combination mechanism Gao, Ruobin Liu, Jiahui Du, Liang Yuen, Kum Fai School of Civil and Environmental Engineering Engineering::Maritime studies Forecast Combination Shipping Market The volatile characteristics of the tanker market pose challenges to forecasting. In addition, the volatile characteristics of newbuilding and secondhand ship prices, time charter rates, and scrap values make developing a unified framework of forecasting difficult. Most researchers have developed forecasting models and evaluated their performance based on a specific market. Such narrow development imposes difficulty for practitioners to choose a suitable model. Due to the boom of machine learning, many researchers are trying to boost the forecasting accuracy of shipping markets using machine learning. However, there are many hyper-parameters of the complex machine learning models and a slight variation of the model may cause significant performance degradation. This paper utilizes a forecast combination mechanism to forecast many time series collected from the shipping market, including newbuilding and secondhand ship prices, scrap values, and time charter rates. The models inside the combination pool are just linear functions. Finally, we compare their performance with conventional machine learning models and naïve forecasts using three error metrics and statistical tests. The statistical tests show that the combination of linear models is superior. The findings of this study also indicate that complex models do not boost forecasting accuracy necessarily. 2022-07-19T04:32:08Z 2022-07-19T04:32:08Z 2021 Journal Article Gao, R., Liu, J., Du, L. & Yuen, K. F. (2021). Shipping market forecasting by forecast combination mechanism. Maritime Policy and Management, 1-16. https://dx.doi.org/10.1080/03088839.2021.1945698 0308-8839 https://hdl.handle.net/10356/160315 10.1080/03088839.2021.1945698 2-s2.0-85109285705 1 16 en Maritime Policy and Management © 2021 Informa UK Limited, trading as Taylor & Francis Group. All rights reserved. |
| spellingShingle | Engineering::Maritime studies Forecast Combination Shipping Market Gao, Ruobin Liu, Jiahui Du, Liang Yuen, Kum Fai Shipping market forecasting by forecast combination mechanism |
| title | Shipping market forecasting by forecast combination mechanism |
| title_full | Shipping market forecasting by forecast combination mechanism |
| title_fullStr | Shipping market forecasting by forecast combination mechanism |
| title_full_unstemmed | Shipping market forecasting by forecast combination mechanism |
| title_short | Shipping market forecasting by forecast combination mechanism |
| title_sort | shipping market forecasting by forecast combination mechanism |
| topic | Engineering::Maritime studies Forecast Combination Shipping Market |
| url | https://hdl.handle.net/10356/160315 |
| work_keys_str_mv | AT gaoruobin shippingmarketforecastingbyforecastcombinationmechanism AT liujiahui shippingmarketforecastingbyforecastcombinationmechanism AT duliang shippingmarketforecastingbyforecastcombinationmechanism AT yuenkumfai shippingmarketforecastingbyforecastcombinationmechanism |