TY - GEN
T1 - Estimated price of shallots commodities national based on parametric and nonparametric approaches
AU - Mardianto, M. Fariz Fadillah
AU - Afifah, Nurul
AU - Safitri, Siti Amelia Dewi
AU - Syahzaqi, Idrus
AU - Sediono,
N1 - Publisher Copyright:
© 2021 American Institute of Physics Inc.. All rights reserved.
PY - 2021/2/26
Y1 - 2021/2/26
N2 - Shallots are one of the leading commodities that strengthen national food security. From 2013 to 2018 the development of shallots production had increased. Except in 2015 the production of shallots decreased by 0.39 percent compared to 2014. Prediction of shallots prices is needed in order to maintain price stability for supporting food security, economic stability, and trade. In predicting the price of shallots commodities, statistical modeling is carried out using parametric and nonparametric time series approaches. However, in this research the parametric approach did not meet the assumption of white noise. Therefore, the nonparametric approach of kernel estimator and Fourier series estimator was used with correlated error. Nonparametric approach is used because it has a flexible form and alternative solutions if the parametric approach does not meet the assumptions. The result was the best model to predict of shallots prices in Indonesia was modeled based on the nonparametric approaches with kernel estimator. The model met goodness criteria like the small MSE value is 757.7224 and the big determination coefficient is 99.95%. The goodness criteria for kernel estimator is better than Fourier series estimator. The kernel estimator has good performance to predict the price of shallots with small MAPE value is 1.088%.
AB - Shallots are one of the leading commodities that strengthen national food security. From 2013 to 2018 the development of shallots production had increased. Except in 2015 the production of shallots decreased by 0.39 percent compared to 2014. Prediction of shallots prices is needed in order to maintain price stability for supporting food security, economic stability, and trade. In predicting the price of shallots commodities, statistical modeling is carried out using parametric and nonparametric time series approaches. However, in this research the parametric approach did not meet the assumption of white noise. Therefore, the nonparametric approach of kernel estimator and Fourier series estimator was used with correlated error. Nonparametric approach is used because it has a flexible form and alternative solutions if the parametric approach does not meet the assumptions. The result was the best model to predict of shallots prices in Indonesia was modeled based on the nonparametric approaches with kernel estimator. The model met goodness criteria like the small MSE value is 757.7224 and the big determination coefficient is 99.95%. The goodness criteria for kernel estimator is better than Fourier series estimator. The kernel estimator has good performance to predict the price of shallots with small MAPE value is 1.088%.
UR - http://www.scopus.com/inward/record.url?scp=85102512229&partnerID=8YFLogxK
U2 - 10.1063/5.0042119
DO - 10.1063/5.0042119
M3 - Conference contribution
AN - SCOPUS:85102512229
T3 - AIP Conference Proceedings
BT - International Conference on Mathematics, Computational Sciences and Statistics 2020
A2 - Alfiniyah, Cicik
A2 - Fatmawati, null
A2 - Windarto, null
PB - American Institute of Physics Inc.
T2 - International Conference on Mathematics, Computational Sciences and Statistics 2020, ICoMCoS 2020
Y2 - 29 September 2020
ER -