{"id":19,"date":"2017-12-05T13:44:02","date_gmt":"2017-12-05T13:44:02","guid":{"rendered":"https:\/\/christian-flender.de\/en\/?page_id=19"},"modified":"2024-10-20T12:05:52","modified_gmt":"2024-10-20T12:05:52","slug":"timeseries","status":"publish","type":"page","link":"https:\/\/christian-flender.de\/en\/timeseries\/","title":{"rendered":"Time Series"},"content":{"rendered":"\n<p>For illustration purposes take historical demands for an arbitrary product. If you know upcoming demands you can plan accordingly (for instance, stock amount and production capacity). The better the forecast, the more accurate the planning.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-style-default\" id=\"block-85318750-72f0-4bce-acb1-bc03d30f6761\"><img loading=\"lazy\" decoding=\"async\" width=\"700\" height=\"450\" src=\"https:\/\/christian-flender.de\/en\/wp-content\/uploads\/sites\/2\/2020\/12\/series1_en.png\" alt=\"\" class=\"wp-image-97\" srcset=\"https:\/\/christian-flender.de\/en\/wp-content\/uploads\/sites\/2\/2020\/12\/series1_en.png 700w, https:\/\/christian-flender.de\/en\/wp-content\/uploads\/sites\/2\/2020\/12\/series1_en-300x193.png 300w\" sizes=\"auto, (max-width: 700px) 100vw, 700px\" \/><figcaption class=\"wp-element-caption\"><em>From the background of features like seasonality and outliers regression analysis determines gradients of trend functions.<\/em><\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large is-style-default\" id=\"block-dfaf1b19-1cac-43b8-88cc-1438f2f99d1f\"><img loading=\"lazy\" decoding=\"async\" width=\"700\" height=\"450\" src=\"https:\/\/christian-flender.de\/en\/wp-content\/uploads\/sites\/2\/2020\/12\/series2_en.png\" alt=\"\" class=\"wp-image-98\" srcset=\"https:\/\/christian-flender.de\/en\/wp-content\/uploads\/sites\/2\/2020\/12\/series2_en.png 700w, https:\/\/christian-flender.de\/en\/wp-content\/uploads\/sites\/2\/2020\/12\/series2_en-300x193.png 300w\" sizes=\"auto, (max-width: 700px) 100vw, 700px\" \/><figcaption class=\"wp-element-caption\">Neural networks learn complex patterns from historical series. They approximate unknown functions instead of determining averaged trends.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large is-style-default\" id=\"block-242e1ab6-9358-406d-8cb8-c2f87f0e2b5c\"><img loading=\"lazy\" decoding=\"async\" width=\"700\" height=\"450\" src=\"https:\/\/christian-flender.de\/en\/wp-content\/uploads\/sites\/2\/2020\/12\/series3_en.png\" alt=\"\" class=\"wp-image-99\" srcset=\"https:\/\/christian-flender.de\/en\/wp-content\/uploads\/sites\/2\/2020\/12\/series3_en.png 700w, https:\/\/christian-flender.de\/en\/wp-content\/uploads\/sites\/2\/2020\/12\/series3_en-300x193.png 300w\" sizes=\"auto, (max-width: 700px) 100vw, 700px\" \/><figcaption class=\"wp-element-caption\">Are time series non-linear and functions not easily determined with traditional methods, neural networks provide an alternative. Deviation between forecast and actual value decreases. Quality of predictions rises.<\/figcaption><\/figure>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>For illustration purposes take historical demands for an arbitrary product. If you know upcoming demands you can plan accordingly (for instance, stock amount and production capacity). The better the forecast, the more accurate the planning.<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-19","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/christian-flender.de\/en\/wp-json\/wp\/v2\/pages\/19","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/christian-flender.de\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/christian-flender.de\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/christian-flender.de\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/christian-flender.de\/en\/wp-json\/wp\/v2\/comments?post=19"}],"version-history":[{"count":2,"href":"https:\/\/christian-flender.de\/en\/wp-json\/wp\/v2\/pages\/19\/revisions"}],"predecessor-version":[{"id":120,"href":"https:\/\/christian-flender.de\/en\/wp-json\/wp\/v2\/pages\/19\/revisions\/120"}],"wp:attachment":[{"href":"https:\/\/christian-flender.de\/en\/wp-json\/wp\/v2\/media?parent=19"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}