Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans
The aim of this study has been to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 is for the process of forecasting time series cost data, while lev...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-08-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1999-4893/11/8/123 |
| _version_ | 1818176549752406016 |
|---|---|
| author | Yamur K. Al-Douri Hussan Hamodi Jan Lundberg |
| author_facet | Yamur K. Al-Douri Hussan Hamodi Jan Lundberg |
| author_sort | Yamur K. Al-Douri |
| collection | DOAJ |
| description | The aim of this study has been to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 is for the process of forecasting time series cost data, while level 2 evaluates the forecasting. Level 1 implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model. Level 2 utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with using the ARIMA model only. The results show the drawbacks of time series forecasting using only the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In level 2, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis. |
| first_indexed | 2024-12-11T20:17:58Z |
| format | Article |
| id | doaj.art-f8949d49b7ac4f05a879d50db5d58730 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-12-11T20:17:58Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-f8949d49b7ac4f05a879d50db5d587302022-12-22T00:52:08ZengMDPI AGAlgorithms1999-48932018-08-0111812310.3390/a11080123a11080123Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel FansYamur K. Al-Douri0Hussan Hamodi1Jan Lundberg2Division of Operation and Maintenance Engineering, Luleå University of Technology, SE-97187 Luleå, SwedenDivision of Operation and Maintenance Engineering, Luleå University of Technology, SE-97187 Luleå, SwedenDivision of Operation and Maintenance Engineering, Luleå University of Technology, SE-97187 Luleå, SwedenThe aim of this study has been to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 is for the process of forecasting time series cost data, while level 2 evaluates the forecasting. Level 1 implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model. Level 2 utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with using the ARIMA model only. The results show the drawbacks of time series forecasting using only the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In level 2, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.http://www.mdpi.com/1999-4893/11/8/123ARIMA modeldata forecastingmulti-objective genetic algorithmregression model |
| spellingShingle | Yamur K. Al-Douri Hussan Hamodi Jan Lundberg Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans Algorithms ARIMA model data forecasting multi-objective genetic algorithm regression model |
| title | Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans |
| title_full | Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans |
| title_fullStr | Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans |
| title_full_unstemmed | Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans |
| title_short | Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans |
| title_sort | time series forecasting using a two level multi objective genetic algorithm a case study of maintenance cost data for tunnel fans |
| topic | ARIMA model data forecasting multi-objective genetic algorithm regression model |
| url | http://www.mdpi.com/1999-4893/11/8/123 |
| work_keys_str_mv | AT yamurkaldouri timeseriesforecastingusingatwolevelmultiobjectivegeneticalgorithmacasestudyofmaintenancecostdatafortunnelfans AT hussanhamodi timeseriesforecastingusingatwolevelmultiobjectivegeneticalgorithmacasestudyofmaintenancecostdatafortunnelfans AT janlundberg timeseriesforecastingusingatwolevelmultiobjectivegeneticalgorithmacasestudyofmaintenancecostdatafortunnelfans |