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Simulation of Autoregressive Integrated Moving Average- Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GARCH) to Forecast Traffic Flow

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@article{IJASEIT14456,
   author = {Challiz D. Omorog},
   title = {Simulation of Autoregressive Integrated Moving Average- Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GARCH) to Forecast Traffic Flow},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {5},
   year = {2021},
   pages = {1825--1831},
   keywords = {ARIMA; GARCH; hybrid algorithm; traffic prediction algorithm.},
   abstract = {Modeling the unprecedented traffic flow data generated by Intelligent Transportation Systems can boost the innovation-capacity of the transportation management systems to drive informed decision-making. Thus, this paper attempts to simulate traffic forecasting techniques that can be adopted in the Philippines to make fact-based decisions into accurate and effective traffic management schemes. In this research, a schematic framework is introduced organized into three stages (Preprocessing, Model Identification and Estimation, and Model Checking) sequentially arranged to comprehensively estimate the best-appropriate model to forecast traffic flow using ARIMA and GARCH models. The Model Identification and Estimation is the conditional stage in the framework that pre-determines if hybrid modeling is necessary based on the given datasets. Various accuracy metrics are also used to find the “best” model and select the optimal values for ARIMA and GARCH models. The proposed framework is simulated in R Programming using the vehicular traffic flow datasets at North Avenue, EDSA northbound, Manila, Philippines. The resulting models, consist of the best fit ARIMA (1,1,3) and GARCH (1,2), are combined as the hybrid model and compared using its prediction results. Based on the visual simulation data, the prediction accuracy result of the ARIMA model outperforms the combined ARIMA-GARCH model given the actual data. Conclusively, the simulation performance provides proof to suggest that the forecasting models are timely tools to predict future traffic flow and aid in making better traffic inventions and schemes.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14456},
   doi = {10.18517/ijaseit.11.5.14456}
}

EndNote

%A Omorog, Challiz D.
%D 2021
%T Simulation of Autoregressive Integrated Moving Average- Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GARCH) to Forecast Traffic Flow
%B 2021
%9 ARIMA; GARCH; hybrid algorithm; traffic prediction algorithm.
%! Simulation of Autoregressive Integrated Moving Average- Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GARCH) to Forecast Traffic Flow
%K ARIMA; GARCH; hybrid algorithm; traffic prediction algorithm.
%X Modeling the unprecedented traffic flow data generated by Intelligent Transportation Systems can boost the innovation-capacity of the transportation management systems to drive informed decision-making. Thus, this paper attempts to simulate traffic forecasting techniques that can be adopted in the Philippines to make fact-based decisions into accurate and effective traffic management schemes. In this research, a schematic framework is introduced organized into three stages (Preprocessing, Model Identification and Estimation, and Model Checking) sequentially arranged to comprehensively estimate the best-appropriate model to forecast traffic flow using ARIMA and GARCH models. The Model Identification and Estimation is the conditional stage in the framework that pre-determines if hybrid modeling is necessary based on the given datasets. Various accuracy metrics are also used to find the “best” model and select the optimal values for ARIMA and GARCH models. The proposed framework is simulated in R Programming using the vehicular traffic flow datasets at North Avenue, EDSA northbound, Manila, Philippines. The resulting models, consist of the best fit ARIMA (1,1,3) and GARCH (1,2), are combined as the hybrid model and compared using its prediction results. Based on the visual simulation data, the prediction accuracy result of the ARIMA model outperforms the combined ARIMA-GARCH model given the actual data. Conclusively, the simulation performance provides proof to suggest that the forecasting models are timely tools to predict future traffic flow and aid in making better traffic inventions and schemes.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14456
%R doi:10.18517/ijaseit.11.5.14456
%J International Journal on Advanced Science, Engineering and Information Technology
%V 11
%N 5
%@ 2088-5334

IEEE

Challiz D. Omorog,"Simulation of Autoregressive Integrated Moving Average- Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GARCH) to Forecast Traffic Flow," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 5, pp. 1825-1831, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.5.14456.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Omorog, Challiz D.
PY  - 2021
TI  - Simulation of Autoregressive Integrated Moving Average- Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GARCH) to Forecast Traffic Flow
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 5
Y2  - 2021
SP  - 1825
EP  - 1831
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - ARIMA; GARCH; hybrid algorithm; traffic prediction algorithm.
N2  - Modeling the unprecedented traffic flow data generated by Intelligent Transportation Systems can boost the innovation-capacity of the transportation management systems to drive informed decision-making. Thus, this paper attempts to simulate traffic forecasting techniques that can be adopted in the Philippines to make fact-based decisions into accurate and effective traffic management schemes. In this research, a schematic framework is introduced organized into three stages (Preprocessing, Model Identification and Estimation, and Model Checking) sequentially arranged to comprehensively estimate the best-appropriate model to forecast traffic flow using ARIMA and GARCH models. The Model Identification and Estimation is the conditional stage in the framework that pre-determines if hybrid modeling is necessary based on the given datasets. Various accuracy metrics are also used to find the “best” model and select the optimal values for ARIMA and GARCH models. The proposed framework is simulated in R Programming using the vehicular traffic flow datasets at North Avenue, EDSA northbound, Manila, Philippines. The resulting models, consist of the best fit ARIMA (1,1,3) and GARCH (1,2), are combined as the hybrid model and compared using its prediction results. Based on the visual simulation data, the prediction accuracy result of the ARIMA model outperforms the combined ARIMA-GARCH model given the actual data. Conclusively, the simulation performance provides proof to suggest that the forecasting models are timely tools to predict future traffic flow and aid in making better traffic inventions and schemes.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14456
DO  - 10.18517/ijaseit.11.5.14456

RefWorks

RT Journal Article
ID 14456
A1 Omorog, Challiz D.
T1 Simulation of Autoregressive Integrated Moving Average- Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GARCH) to Forecast Traffic Flow
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 5
YR 2021
SP 1825
OP 1831
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 ARIMA; GARCH; hybrid algorithm; traffic prediction algorithm.
AB Modeling the unprecedented traffic flow data generated by Intelligent Transportation Systems can boost the innovation-capacity of the transportation management systems to drive informed decision-making. Thus, this paper attempts to simulate traffic forecasting techniques that can be adopted in the Philippines to make fact-based decisions into accurate and effective traffic management schemes. In this research, a schematic framework is introduced organized into three stages (Preprocessing, Model Identification and Estimation, and Model Checking) sequentially arranged to comprehensively estimate the best-appropriate model to forecast traffic flow using ARIMA and GARCH models. The Model Identification and Estimation is the conditional stage in the framework that pre-determines if hybrid modeling is necessary based on the given datasets. Various accuracy metrics are also used to find the “best” model and select the optimal values for ARIMA and GARCH models. The proposed framework is simulated in R Programming using the vehicular traffic flow datasets at North Avenue, EDSA northbound, Manila, Philippines. The resulting models, consist of the best fit ARIMA (1,1,3) and GARCH (1,2), are combined as the hybrid model and compared using its prediction results. Based on the visual simulation data, the prediction accuracy result of the ARIMA model outperforms the combined ARIMA-GARCH model given the actual data. Conclusively, the simulation performance provides proof to suggest that the forecasting models are timely tools to predict future traffic flow and aid in making better traffic inventions and schemes.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14456
DO  - 10.18517/ijaseit.11.5.14456