TY - GEN
T1 - Hybrid multivariate generalized space-time autoregressive artificial neural network models to forecast air pollution data at Surabaya
AU - Pusporani, Elly
AU - Suhartono,
AU - Prastyo, Dedy Dwi
N1 - Publisher Copyright:
© 2019 Author(s).
PY - 2019/12/18
Y1 - 2019/12/18
N2 - Many time series data have both time and space dimension which is known as space-time data. The objective of this research is to propose a hybrid Multivariate Generalized Space-Time Autoregressive Artificial Neural Network (MGSTAR- ANN) for handling both linear and nonlinear pattern in space-time data forecast. Air pollution data is used as a case study. The data consist of three pollutants, i.e. CO, NO2, and PM10 that were observed at three different locations, i.e. SUF 1, SUF 6, and SUF 7. RMSE (Root Mean Square Error) is used as an accuracy measurement for selecting the best model. The results show that a hybrid MGSTAR-ANN yield more accurate forecast than MGSTAR model. Moreover, these results are in line with one out of five major findings in the M4-Competition reported that the hybrid approach which utilized both statistical and Machine Learning features have more accurate result than the combination benchmark used to compare the submitted methods.
AB - Many time series data have both time and space dimension which is known as space-time data. The objective of this research is to propose a hybrid Multivariate Generalized Space-Time Autoregressive Artificial Neural Network (MGSTAR- ANN) for handling both linear and nonlinear pattern in space-time data forecast. Air pollution data is used as a case study. The data consist of three pollutants, i.e. CO, NO2, and PM10 that were observed at three different locations, i.e. SUF 1, SUF 6, and SUF 7. RMSE (Root Mean Square Error) is used as an accuracy measurement for selecting the best model. The results show that a hybrid MGSTAR-ANN yield more accurate forecast than MGSTAR model. Moreover, these results are in line with one out of five major findings in the M4-Competition reported that the hybrid approach which utilized both statistical and Machine Learning features have more accurate result than the combination benchmark used to compare the submitted methods.
UR - http://www.scopus.com/inward/record.url?scp=85077681470&partnerID=8YFLogxK
U2 - 10.1063/1.5139822
DO - 10.1063/1.5139822
M3 - Conference contribution
AN - SCOPUS:85077681470
T3 - AIP Conference Proceedings
BT - 2nd International Conference on Science, Mathematics, Environment, and Education
A2 - Indriyanti, Nurma Yunita
A2 - Ramli, Murni
A2 - Nurhasanah, Farida
PB - American Institute of Physics Inc.
T2 - 2nd International Conference on Science, Mathematics, Environment, and Education, ICoSMEE 2019
Y2 - 26 July 2019 through 28 July 2019
ER -