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Generalized STAR (1;1) Model with Outlier – Case Study of Begal in Medan, North Sumatera
Masteriana D.a, Riani M.I.a, Mukhaiyar U.a
a Mathematics Department, Institut Teknologi Bandung, 40132, 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]© Published under licence by IOP Publishing Ltd.This study was aimed to compare generalized space time autoregressive (GSTAR) (1;1) model with its modification. The modification of GSTAR (1;1) model was done by adding the outlier factor which gained by outlier detection procedure. A case study was done towards the amount of monthly crime activity-begal at seven police sectors in Medan, North Sumatera. By using three steps of Box-Jenkins, Autoregressive (1) model was applied to each location and resulted West Medan as fifth location with additive outlier (AO) in the third data of time which weighted -57.5247. The comparison of model was done by calculating the root mean square of error (RMSE) in each model. Since GSTAR (1:1) model has larger RMSE (8.1728) rather than its modification (7.9700), then GSTAR (1;1) model with outlier is the best model in this case study. This invention shows that the model of space time with outlier model is useable to find precise result rather than space time model only.[/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]Additive outliers,Auto-regressive,Box-Jenkins,Comparison of models,Outlier case,Outlier detection procedure,Root Mean Square,Space-time model[/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][/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/10.1088/1742-6596/1245/1/012046[/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]