BibTex Citation Data :
@article{Medstat8490, author = {Sufia Janah and Winita Sulandari and Santoso Wiyono}, title = {PENERAPAN MODEL HYBRID ARIMA BACKPROPAGATION UNTUK PERAMALAN HARGA GABAH INDONESIA}, journal = {MEDIA STATISTIKA}, volume = {7}, number = {2}, year = {2014}, keywords = {}, abstract = { Hybrid model discussed in this paper combining ARIMA and backpropagation is applied to grain price forecasting in Indonesia for period January 2008 until April 2013. The grain price time series consists of linear and nonlinear patterns. Backpropagations can recognize non linear patterns that can not be done by ARIMA. In order to find the best model, some combinations of prepocessing transformations, the number of input and hidden units, and the activation function were applied in the contruction of the network structure. Based on the experiments, it can be showed that ARIMA backpropagation hybrid model provides more accurate results than ARIMA model. The hybrid model would rather be used in the short-term forecasting, no more than three periods. Keywords : ARIMA, Backpropagation, Hybrid, Grain Price }, issn = {2477-0647}, pages = {63--69} doi = {10.14710/medstat.7.2.63-69}, url = {https://ejournal.undip.ac.id/index.php/media_statistika/article/view/8490} }
Refworks Citation Data :
Hybrid model discussed in this paper combining ARIMA and backpropagation is applied to grain price forecasting in Indonesia for period January 2008 until April 2013. The grain price time series consists of linear and nonlinear patterns. Backpropagations can recognize non linear patterns that can not be done by ARIMA. In order to find the best model, some combinations of prepocessing transformations, the number of input and hidden units, and the activation function were applied in the contruction of the network structure. Based on the experiments, it can be showed that ARIMA backpropagation hybrid model provides more accurate results than ARIMA model. The hybrid model would rather be used in the short-term forecasting, no more than three periods.
Keywords: ARIMA, Backpropagation, Hybrid, Grain Price
Article Metrics:
Last update:
Forecasting the Consumer Price Index in the Regions of the Philippines using Machine Learning for Time Series Models
Analyzing and Forecasting Admission data using Time Series Model
Peramalan Jumlah Kasus COVID-19 di Provinsi Sulawesi Barat Menggunakan Model Hybrid ARIMA Backpropagation
Last update: 2024-11-21 05:09:30
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Media Statistika journal and Department of Statistics, Universitas Diponegoro as the publisher of the journal. Copyright encompasses the rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations.
Media Statistika journal and Department of Statistics, Universitas Diponegoro and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in Media Statistika journal are the sole and exclusive responsibility of their respective authors and advertisers.
The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form Media Statistika]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :
Dr. Di Asih I Maruddani (Editor-in-Chief) Editorial Office of Media StatistikaDepartment of Statistics, Universitas DiponegoroJl. Prof. Soedarto, Kampus Undip Tembalang, Semarang, Central Java, Indonesia 50275Telp./Fax: +62-24-7474754Email: maruddani@live.undip.ac.id
Media Statistika
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
Gedung F Lantai 3, Jalan Prof Jacub Rais, Kampus Tembalang
Semarang 50275
Indexing: