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Non-stationary model for rice prices in Bandung, Indonesia
Setiyowati S.a, Pasaribu U.S.a, Mukhaiyar U.a
a Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]A non-stationary forecasting model for rice prices is proposed in this paper. A daily rice price has two behaviors i.e. normal and spiky. To encounter both behaviors, non-stationary model has been applied. From various type of non-stationary model, the simple one which contains ARIMA model and GARCH model are applied. The ARIMA model is applied to catch the linear relationship of non-stationary data through differencing. On the other hand, the GARCH model is applied to unveil the heteroscedastic character of residuals with inconstant variance, a generalization of ARCH models. However, due to its limitation of recent GARCH model, MATLAB software and NUMXL, additional software on Excel, shall be used for parameters estimation of identified models. Hereafter, the forecasting of normal and spike patterns of rice prices are generated to form an overall price up to one day-ahead. As a result, this forecasting of the rice price could provide extensive and valuable information for rice market stakeholders. © 2013 IEEE.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]ARIMA,Forecasting modeling,GARCH,Linear relationships,Non-stationary model,Nonstationary,Parameters estimation,spiky behavior[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]ARCH,ARIMA,forecasting,GARCH,non-stationary,spiky behavior[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICA.2013.6734088[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]