TY - JOUR
T1 - Hierarchical time series bottom-up approach for forecast the export value in Central Java
AU - Mahkya, D. A.
AU - Ulama, B. S.
AU - Suhartono,
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2017/10/28
Y1 - 2017/10/28
N2 - The purpose of this study is Getting the best modeling and predicting the export value of Central Java using a Hierarchical Time Series. The export value is one variable injection in the economy of a country, meaning that if the export value of the country increases, the country's economy will increase even more. Therefore, it is necessary appropriate modeling to predict the export value especially in Central Java. Export Value in Central Java are grouped into 21 commodities with each commodity has a different pattern. One approach that can be used time series is a hierarchical approach. Hierarchical Time Series is used Buttom-up. To Forecast the individual series at all levels using Autoregressive Integrated Moving Average (ARIMA), Radial Basis Function Neural Network (RBFNN), and Hybrid ARIMA-RBFNN. For the selection of the best models used Symmetric Mean Absolute Percentage Error (sMAPE). Results of the analysis showed that for the Export Value of Central Java, Bottom-up approach with Hybrid ARIMA-RBFNN modeling can be used for long-term predictions. As for the short and medium-term predictions, it can be used a bottom-up approach RBFNN modeling. Overall bottom-up approach with RBFNN modeling give the best result.
AB - The purpose of this study is Getting the best modeling and predicting the export value of Central Java using a Hierarchical Time Series. The export value is one variable injection in the economy of a country, meaning that if the export value of the country increases, the country's economy will increase even more. Therefore, it is necessary appropriate modeling to predict the export value especially in Central Java. Export Value in Central Java are grouped into 21 commodities with each commodity has a different pattern. One approach that can be used time series is a hierarchical approach. Hierarchical Time Series is used Buttom-up. To Forecast the individual series at all levels using Autoregressive Integrated Moving Average (ARIMA), Radial Basis Function Neural Network (RBFNN), and Hybrid ARIMA-RBFNN. For the selection of the best models used Symmetric Mean Absolute Percentage Error (sMAPE). Results of the analysis showed that for the Export Value of Central Java, Bottom-up approach with Hybrid ARIMA-RBFNN modeling can be used for long-term predictions. As for the short and medium-term predictions, it can be used a bottom-up approach RBFNN modeling. Overall bottom-up approach with RBFNN modeling give the best result.
UR - http://www.scopus.com/inward/record.url?scp=85034571624&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/893/1/012033
DO - 10.1088/1742-6596/893/1/012033
M3 - Conference article
AN - SCOPUS:85034571624
SN - 1742-6588
VL - 893
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012033
T2 - Asian Mathematical Conference 2016, AMC 2016
Y2 - 25 July 2016 through 29 July 2016
ER -