{"id":372,"date":"2014-05-13T10:52:31","date_gmt":"2014-05-13T10:52:31","guid":{"rendered":"http:\/\/kourentzes.com\/forecasting\/?p=372"},"modified":"2015-09-23T19:08:54","modified_gmt":"2015-09-23T19:08:54","slug":"forecast-combinations-for-intermittent-demand","status":"publish","type":"post","link":"https:\/\/kourentzes.com\/forecasting\/2014\/05\/13\/forecast-combinations-for-intermittent-demand\/","title":{"rendered":"Forecast Combinations for Intermittent Demand"},"content":{"rendered":"<p style=\"text-align: justify;\">F. Petropoulos and N. Kourentzes, 2015, Journal of Operational Research Society, 66: 914-924. <a href=\"http:\/\/dx.doi.org\/10.1057\/jors.2014.62\">http:\/\/dx.doi.org\/10.1057\/jors.2014.62<\/a><\/p>\n<p style=\"text-align: justify;\">Intermittent demand is characterised by infrequent demand arrivals, where many periods have zero demand, coupled with varied demand sizes. The dual source of variation renders forecasting for intermittent demand a very challenging task. Many researchers have focused on the development of specialised methods for intermittent demand. However, apart from a case study on hierarchical forecasting, the effects of combining, which is a standard practice for regular demand, have not been investigated. This paper empirically explores the efficiency of forecast combinations in the intermittent demand context. We examine both method and temporal combinations of forecasts. The first are based on combinations of different methods on the same time series, while the latter use combinations of forecasts produced on different views of the time series, based on temporal aggregation. Temporal combinations of single or multiple methods are investigated, leading to a new time series classification, which leads to model selection and combination. Results suggest that appropriate combinations lead to improved forecasting performance over single methods, as well as simplifying the forecasting process by limiting the need for manual selection of methods or hyper-parameters of good performing benchmarks. This has direct implications for intermittent demand forecasting in practice.<\/p>\n<p>Download <a href=\"http:\/\/kourentzes.com\/forecasting\/wp-content\/uploads\/2014\/05\/Petropoulos-Kourentzes-2014-Forecast-Combinations-for-Intermittent-Demand.pdf\">paper<\/a>.<br \/>\nA simplified discussion of the article can be found <a href=\"http:\/\/kourentzes.com\/forecasting\/2014\/05\/26\/improving-forecasting-via-multiple-temporal-aggregation\/\">here<\/a>.<br \/>\nSome updated results can be found <a title=\"MAPA and intermittent demand forecasting\" href=\"http:\/\/kourentzes.com\/forecasting\/2014\/07\/17\/mapa-and-intermittent-demand-forecasting\/\">here<\/a>.<\/p>\n<div class=\"SPOSTARBUST-Related-Posts\"><H3>Related Posts<\/H3><ul class=\"entry-meta\"><li class=\"SPOSTARBUST-Related-Post\"><a title=\"Special issue on innovations in hierarchical forecasting\" href=\"https:\/\/kourentzes.com\/forecasting\/2020\/10\/25\/special-issue-on-innovations-in-hierarchical-forecasting\/\" rel=\"bookmark\">Special issue on innovations in hierarchical forecasting<\/a><\/li>\n<li class=\"SPOSTARBUST-Related-Post\"><a title=\"Intermittent demand &#038; THieF &#8211; EJOR Editors\u2019 Choice Articles\" href=\"https:\/\/kourentzes.com\/forecasting\/2020\/06\/08\/intermittent-demand-thief-ejor-editors-choice-articles\/\" rel=\"bookmark\">Intermittent demand &#038; THieF &#8211; EJOR Editors\u2019 Choice Articles<\/a><\/li>\n<li class=\"SPOSTARBUST-Related-Post\"><a title=\"Elucidate structure in intermittent demand time series\" href=\"https:\/\/kourentzes.com\/forecasting\/2020\/05\/25\/elucidate-structure-in-intermittent-demand-time-series\/\" rel=\"bookmark\">Elucidate structure in intermittent demand time series<\/a><\/li>\n<\/ul><\/div><!-- AddThis Advanced Settings generic via filter on the_content --><!-- AddThis Share Buttons generic via filter on the_content -->","protected":false},"excerpt":{"rendered":"<p>F. Petropoulos and N. Kourentzes, 2015, Journal of Operational Research Society, 66: 914-924. http:\/\/dx.doi.org\/10.1057\/jors.2014.62<!-- AddThis Advanced Settings generic via filter on get_the_excerpt --><!-- AddThis Share Buttons generic via filter on get_the_excerpt --><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[5],"tags":[22,27,38,43,36],"_links":{"self":[{"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/posts\/372"}],"collection":[{"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/comments?post=372"}],"version-history":[{"count":0,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/posts\/372\/revisions"}],"wp:attachment":[{"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/media?parent=372"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/categories?post=372"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/tags?post=372"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<!-- WP Super Cache is installed but broken. 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