Preprint Article Version 1 This version is not peer-reviewed

Time Series Forecasting Using a Two-level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans

Version 1 : Received: 22 June 2018 / Approved: 24 June 2018 / Online: 24 June 2018 (07:48:49 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Algorithms 2018, 11, 123
DOI: 10.3390/a11080123

Abstract

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.

Subject Areas

ARIMA model; data forecasting; multi-objective genetic algorithm; regression model

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