{"id":2139,"date":"2022-10-27T16:29:20","date_gmt":"2022-10-27T09:29:20","guid":{"rendered":"http:\/\/research.binus.ac.id\/airdc\/?p=2139"},"modified":"2022-10-27T16:29:20","modified_gmt":"2022-10-27T09:29:20","slug":"evolving-hybrid-generalized-space-time-autoregressive-forecasting-with-cascade-neural-network-particle-swarm-optimization","status":"publish","type":"post","link":"https:\/\/research.binus.ac.id\/airdc\/2022\/10\/evolving-hybrid-generalized-space-time-autoregressive-forecasting-with-cascade-neural-network-particle-swarm-optimization\/","title":{"rendered":"Evolving Hybrid Generalized Space-Time Autoregressive Forecasting with Cascade Neural Network Particle Swarm Optimization"},"content":{"rendered":"<p style=\"text-align: justify\">Background: The generalized space-time autoregressive (GSTAR) model is one of the most widely used models for modeling and forecasting time series and location data. Methods: In the GSTAR model, there is an assumption that the research locations are heterogeneous. In addition, the differences between these locations are shown in the form of a weighting matrix. The novelty of this paper is that we propose the hybrid time-series model of GSTAR uses the cascade neural network and obtains the best parameters from particle swarm optimization. Results and conclusion: This hybrid model provides a high accuracy value for forecasting PM2.5, PM10, NOx, and SO2 with high accuracy forecasting, which is justified by a mean absolute percentage error (MAPE) accuracy of around 0.01%.<\/p>\n<p style=\"text-align: justify\">Atmosphere<\/p>\n<p style=\"text-align: justify\"><strong>Toni Toharudin, Rezzy Eko Caraka, Hasbi Yasin, Bens Pardamean<\/strong><\/p>\n<p style=\"text-align: justify\"><a href=\"https:\/\/www.researchgate.net\/publication\/360905128_Evolving_Hybrid_Generalized_Space-Time_Autoregressive_Forecasting_with_Cascade_Neural_Network_Particle_Swarm_Optimization\">Read Full Paper<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Background: The generalized space-time autoregressive (GSTAR) model is one of the most widely used models for modeling and forecasting time series and location data. Methods: In the GSTAR model, there is an assumption that the research locations are heterogeneous. In addition, the differences between these locations are shown in the form of a weighting matrix. [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":2140,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-2139","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications"],"_links":{"self":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/2139","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/users\/14"}],"replies":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/comments?post=2139"}],"version-history":[{"count":1,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/2139\/revisions"}],"predecessor-version":[{"id":2141,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/2139\/revisions\/2141"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/media\/2140"}],"wp:attachment":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/media?parent=2139"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/categories?post=2139"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/tags?post=2139"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}