{"id":23467,"date":"2026-09-01T12:14:57","date_gmt":"2026-09-01T09:14:57","guid":{"rendered":"https:\/\/grnet.gr\/?p=23467"},"modified":"2026-09-01T12:14:57","modified_gmt":"2026-09-01T09:14:57","slug":"pharos-training-series-course-14-machine-deep-learning-for-time-series-forecasting-on-september-15th-2026","status":"publish","type":"post","link":"https:\/\/grnet.gr\/en\/2026\/09\/01\/pharos-training-series-course-14-machine-deep-learning-for-time-series-forecasting-on-september-15th-2026\/","title":{"rendered":"PHAROS Training Series &#8211; Course 14 &#8220;Machine &#038; Deep Learning for Time Series forecasting&#8221;, on September 15th, 2026"},"content":{"rendered":"<p style=\"font-weight: 400;\"><strong>PHAROS AI Factory <\/strong>announces the <strong>14th Course <\/strong>of its Training Series, under the title &#8220;<strong>Machine &amp; Deep Learning for Time Series forecasting<\/strong>&#8220;, under the<strong>topic Deep Learning<\/strong>, held online via Zoom on September 15th, 2026.<\/p>\n<p style=\"font-weight: 400;\">\n<p style=\"font-weight: 400;\"><strong>Presentation language<\/strong>: Greek<\/p>\n<p style=\"font-weight: 400;\">\n<p style=\"font-weight: 400;\"><strong>Course Description<\/strong>: This hands-on session works through a complete forecasting pipeline in Python related to time-series data. Participants begin with preprocessing (handling missing values, outliers, stationarity, and feature engineering), then move through classical machine learning models, gradient-boosted trees, and deep learning architectures including LSTMs, temporal convolutional networks, and Transformer-based models such as PatchTST. The course is built around realistic use cases, working with live coding alongside participants.<\/p>\n<p style=\"font-weight: 400;\">\n<p style=\"font-weight: 400;\"><strong>Audience<\/strong>: ML Engineers, Data Scientists, Academic Researchers, Business Owners<\/p>\n<p style=\"font-weight: 400;\">\n<p style=\"font-weight: 400;\"><strong>Location<\/strong>: Online via Zoom (you will get the zoom link upon registration)<\/p>\n<p style=\"font-weight: 400;\">\n<p style=\"font-weight: 400;\"><strong>Learning Objectives<\/strong>:<\/p>\n<p style=\"font-weight: 400;\">By the end of the course, participants will be able to:<\/p>\n<ul style=\"font-weight: 400;\">\n<li>Prepare time series data for supervised learning (handling irregular sampling, missing values and outliers \u2014 while avoiding the data leakage patterns that invalidate most forecasting results).<\/li>\n<li>Select appropriate baselines and evaluation metrics, and apply walk-forward validation rather than standard cross-validation.<\/li>\n<li>Build and compare classical, gradient-boosting and deep learning forecasting models, and judge when the added complexity of deep learning is justified.<\/li>\n<li>Recognise the strengths and limitations of current architectures, from LSTMs and TCNs to Transformer-based and foundation models.<\/li>\n<\/ul>\n<p style=\"font-weight: 400;\">\n<p style=\"font-weight: 400;\">The course\u2019s agenda can be found <a href=\"https:\/\/events.grnet.gr\/event\/219\/timetable\/\">here<\/a>.<\/p>\n<p style=\"font-weight: 400;\"><strong>Register to attend by filling out the form <\/strong><a href=\"https:\/\/events.grnet.gr\/event\/219\/registrations\/157\/\"><strong>here<\/strong><\/a><strong>.\u00a0<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>PHAROS AI Factory announces the 14th Course of its Training Series, under the title &#8220;Machine &amp; Deep Learning for Time Series forecasting&#8220;, under thetopic Deep Learning, held online via Zoom on September 15th, 2026. Presentation language: Greek Course Description: This hands-on session works through a complete forecasting pipeline in Python related to time-series data. Participants [&hellip;]<\/p>\n","protected":false},"author":6,"featured_media":23468,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[294,355,490],"tags":[],"class_list":["post-23467","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-events-en","category-news-en","category-news_en-en"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/posts\/23467","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/comments?post=23467"}],"version-history":[{"count":1,"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/posts\/23467\/revisions"}],"predecessor-version":[{"id":23470,"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/posts\/23467\/revisions\/23470"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/media\/23468"}],"wp:attachment":[{"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/media?parent=23467"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/categories?post=23467"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/tags?post=23467"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}