Evolutionary Based ARIMA Models for Stock Price Forecasting

Vantuch, Tomáš; Zelinka, Ivan

doi:10.1007/978-3-319-10759-2_25

“…The literature cited present different methodologies for forecasting compared with our methods. Vantuch & Zelinka [11], Ervural et al [13] and Hatzakis & Wallace [6] used GAs with the ARIMA model to improve results compared with use of the ARIMA model alone. These authors used GAs to obtain a randomization of the ARIMA parameters, which is not always sufficient because ARIMA parameter estimation should be based on the data and research problem to achieve better forecasting.…”

Section: Results Of the Multi-objective Genetic Algorithms (Gas) Basementioning

confidence: 99%

“…Vantuch and Zelinka [11] modified the ARIMA model based on the genetic algorithm and particle swarm optimization (PSO) to estimate and predict data of time. They found that the genetic algorithm could find a suitable ARIMA model and pointed to improvements through individual binary randomization for every parameter input of the ARIMA model.…”

Section: Introductionmentioning

confidence: 99%

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

Al-Douri

¹

,

Hamodi

²

,

Lundberg

³

2018

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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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“…Vantuch & Zelinka [8] modified the ARIMA model based on the genetic algorithm and particle swarm optimization (PSO) to estimate and predict data of time. They found that the genetic algorithm could find a suitable ARIMA model and pointed to improvements through individual binary randomization for every parameter input of the ARIMA model.…”

Section: Of 18mentioning

confidence: 99%

“…The main part of the ARIMA model concerns the combination of autoregression (AR) and moving-average (MA) polynomials into a complex polynomial, as seen in the equation below [8]. The ARIMA model is applied to all the data points for each cost data object (labour and material).…”

Section: The Arima Modelmentioning

confidence: 99%

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

Al-Douri¹,

Hamodi²,

Lundberg³

2018

Preprint

6

0

View full text Add to dashboard Cite

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.

show abstract