preprints.org > engineering > general engineering > doi: 10.20944/preprints201806.0365.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

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.

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