Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans
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Division of Operation and Maintenance Engineering, Luleå University of Technology, SE-97187 Luleå, Sweden
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Mechanical Engineering Department, College of Engineering, University of Mosul, Mosul AZ 6321, Iraq
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Author to whom correspondence should be addressed.
Algorithms 2018, 11(8), 123; https://doi.org/10.3390/a11080123
Received: 22 June 2018 / Revised: 2 August 2018 / Accepted: 7 August 2018 / Published: 13 August 2018
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.
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MDPI and ACS Style
Al-Douri, Y.K.; Hamodi, H.; Lundberg, J. Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans. Algorithms 2018, 11, 123.
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