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

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 123 ◽  
Author(s):  
Yamur Al-Douri ◽  
Hussan Hamodi ◽  
Jan Lundberg
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Regression Model ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Level 1 ◽  
Dynamic Regression Model ◽  
Level 2

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.

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

2018 ◽  
Author(s):  
Yamur K. Al-Douri ◽  
Hussan Hamodi ◽  
Jan Lundberg
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Regression Model ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Level 1 ◽  
Dynamic Regression Model ◽  
Level 2

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.

Forecasting stock market price by using fuzzified Choquet integral based fuzzy measures with genetic algorithm for parameter optimization

2020 ◽  
Vol 54 (2) ◽  
pp. 597-614
Author(s):  
Shanoli Samui Pal ◽  
Samarjit Kar
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Linear Regression ◽  
Regression Model ◽  
Choquet Integral ◽  
Time Series Forecasting ◽  
Fuzzy Measure ◽  
Model Parameters ◽  
Forecasting Models ◽  
Non Linear

In this paper, fuzzified Choquet integral and fuzzy-valued integrand with respect to separate measures like fuzzy measure, signed fuzzy measure and intuitionistic fuzzy measure are used to develop regression model for forecasting. Fuzzified Choquet integral is used to build a regression model for forecasting time series with multiple attributes as predictor attributes. Linear regression based forecasting models are suffering from low accuracy and unable to approximate the non-linearity in time series. Whereas Choquet integral can be used as a general non-linear regression model with respect to non classical measures. In the Choquet integral based regression model parameters are optimized by using a real coded genetic algorithm (GA). In these forecasting models, fuzzified integrands denote the participation of an individual attribute or a group of attributes to predict the current situation. Here, more generalized Choquet integral, i.e., fuzzified Choquet integral is used in case of non-linear time series forecasting models. Three different real stock exchange data are used to predict the time series forecasting model. It is observed that the accuracy of prediction models highly depends on the non-linearity of the time series.

Goodness-of-Fit of Time Series Model for the Storage of Water in Agricultural Reservoir

2005 ◽  
Vol 277-279 ◽  
pp. 200-205
Author(s):  
Hoon Ja Lee ◽  
Tae Jin Ahn
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Regression Model ◽  
Goodness Of Fit ◽  
Arima Model ◽  
Error Model ◽  
Time Series Model ◽  
Efficient Management ◽  
Agricultural Reservoir ◽  
Dynamic Regression Model

The efficient management of the agricultural reservoir may well supply stable water for irrigation. In this article, time series analysis has been used for analyzing the storage of water data in Kihung agricultural reservoir that is located in Yongin City, Korea. For analyzing the storage of water data, three models, the ARIMA model, the autoregressive error model, and the dynamic regression model have been used. The result shows that the autoregressive error model is best suited for describing the storage of water data.

scholarly journals Unified Multi-Objective Genetic Algorithm for Energy Efficient Job Shop Scheduling

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hongjing Wei ◽  
Shaobo Li ◽  
Huafeng Quan ◽  
Dacheng Liu ◽  
Shu Rao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Efficient ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm
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