{"id":921,"date":"2015-10-10T15:38:00","date_gmt":"2015-10-10T15:38:00","guid":{"rendered":"http:\/\/kourentzes.com\/forecasting\/?p=921"},"modified":"2015-10-10T15:43:49","modified_gmt":"2015-10-10T15:43:49","slug":"interactive-simple-exponential-smoothing","status":"publish","type":"post","link":"https:\/\/kourentzes.com\/forecasting\/2015\/10\/10\/interactive-simple-exponential-smoothing\/","title":{"rendered":"Interactive simple exponential smoothing"},"content":{"rendered":"<p style=\"text-align: justify;\">Another interactive demo I created for the courses I teach. This one is about simple exponential smoothing and the main objective is to show the interaction between smoothing parameter and initial level in the fitting and holdout samples. A different interactive demo about exponential smoothing can be found <a href=\"http:\/\/kourentzes.com\/forecasting\/2014\/10\/30\/exponential-smoothing-demo\/\">here<\/a>. A couple of points that may be interesting to observe:<\/p>\n<ul>\n<li style=\"text-align: justify;\">In-sample and holdout errors do not behave in the same way.<\/li>\n<li style=\"text-align: justify;\">The error curves can be substantially different for various initial level values.<\/li>\n<\/ul>\n<p><iframe loading=\"lazy\" id=\"SES\" style=\"border: none; width: 100%; height: 485px;\" src=\"https:\/\/kourentzes.shinyapps.io\/shinySES\" width=\"300\" height=\"150\" frameborder=\"0\"><\/iframe><\/p>\n<div class=\"SPOSTARBUST-Related-Posts\"><H3>Related Posts<\/H3><ul class=\"entry-meta\"><li class=\"SPOSTARBUST-Related-Post\"><a title=\"Automatic robust estimation for exponential smoothing: perspectives from statistics and machine learning\" href=\"https:\/\/kourentzes.com\/forecasting\/2020\/06\/04\/automatic-robust-estimation-for-exponential-smoothing-perspectives-from-statistics-and-machine-learning\/\" rel=\"bookmark\">Automatic robust estimation for exponential smoothing: perspectives from statistics and machine learning<\/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<li class=\"SPOSTARBUST-Related-Post\"><a title=\"Optimising forecasting models for inventory planning\" href=\"https:\/\/kourentzes.com\/forecasting\/2020\/05\/25\/optimising-forecasting-models-for-inventory-planning\/\" rel=\"bookmark\">Optimising forecasting models for inventory planning<\/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>Another interactive demo I created for the courses I teach. This one is about simple exponential smoothing and the main objective is to show the interaction between smoothing parameter and initial level in the fitting and holdout samples. A different interactive demo about exponential smoothing can be found here. A couple of points that may\u2026 <span class=\"read-more\"><a href=\"https:\/\/kourentzes.com\/forecasting\/2015\/10\/10\/interactive-simple-exponential-smoothing\/\">Read More &raquo;<\/a><\/span><!-- 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":[41],"tags":[32,23,53],"_links":{"self":[{"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/posts\/921"}],"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=921"}],"version-history":[{"count":0,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/posts\/921\/revisions"}],"wp:attachment":[{"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/media?parent=921"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/categories?post=921"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/tags?post=921"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<!-- WP Super Cache is installed but broken. The constant WPCACHEHOME must be set in the file wp-config.php and point at the WP Super Cache plugin directory. -->