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.
Highlights
Time series forecasting predicts future data points based on observed data over a period known as the lead-time
It has been established that the autoregressive integrated moving average (ARIMA) model and the multi-objective genetic algorithm (GA) based on the dynamic regression model have drawbacks when used to forecast data for two cost objects
The normalization performed in the multi-objective GA based on the dynamic regression model aims to decrease the computational complexity
Summary
Time series forecasting predicts future data points based on observed data over a period known as the lead-time. The purpose of forecasting data points is to provide a basis for economic planning, production planning, production control and optimizing industrial processes. Much effort has been devoted over the past few decades to the development and improvement of time series forecasting models [3]. Traditional models for time series forecasting, such as the Box-Jenkins or autoregressive integrated moving average (ARIMA) model, assume that the studied time series are generated from linear processes. These models may be inappropriate if the underlying mechanism is nonlinear
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