{"id":5236,"date":"2025-03-25T13:25:44","date_gmt":"2025-03-25T12:25:44","guid":{"rendered":"https:\/\/www.hpc.mk\/?post_type=event_listing&p=5236"},"modified":"2025-04-10T11:45:16","modified_gmt":"2025-04-10T09:45:16","slug":"advancing-time-series-forecasting-exploring-the-potential-of-generative-ai","status":"publish","type":"event_listing","link":"https:\/\/www.hpc.mk\/index.php\/event\/advancing-time-series-forecasting-exploring-the-potential-of-generative-ai\/","title":{"rendered":"Artificial Intelegence – Let’s Talk Abot The Future"},"content":{"rendered":"\n

HPC North Macedonia, in collaboration with its partners, is excited to announce a brand-new series of events, workshops, and podcasts<\/strong>. <\/p>\n\n\n\n

<\/div>\n\n\n\n

The first event in this series will be a\u00a0webinar on April 2, 2025, at 18:00. It will take place at\u00a0FINKI Amphitheatre (Small)(Faculty of Computer Science and Engineering)\u00a0and will also be available online for remote participants.<\/p>\n\n\n\n

<\/div>\n\n\n\n

The webinar will dive into two fascinating topics:<\/p>\n\n\n\n

<\/div>\n\n\n\n

Prof. Gjorgji Madjarov<\/p>\n\n\n\n

Title:<\/strong> Advancing Time-Series Forecasting & Exploring the Potential of Generative AI<\/p>\n\n\n\n

Abstract: Time-series forecasting is fundamental to various industries, enabling informed decision-making in finance, healthcare, and supply chain management. This presentation provides a comprehensive overview of both traditional and modern approaches to time-series analysis. We will begin with classical methods such as ARIMA and Exponential Smoothing, discussing their strengths and limitations. Next, we will explore advancements in machine learning and deep learning approaches, including, transformers, and probabilistic forecasting techniques. Finally, we will examine the emerging role of generative AI in forecasting, uncertainty modelling, and foundation models for time-series prediction.<\/p>\n\n\n\n

<\/div>\n\n\n\n

Prof. Dimitar Trajanov and Dimitar Peshevski<\/p>\n\n\n\n

Title: <\/strong>Causal AI<\/p>\n\n\n\n

Abstract: In this presentation, we demonstrate how machine learning and AI can power causal inference. We explain that while traditional predictive models are excellent at forecasting outcomes, they may capture relationships that do not hold under intervention. We illustrate the difference between correlation and causation with clear examples and discuss classical methods\u2014such as re-weighting, stratification, and matching\u2014to address issues like confounding and selection bias. Finally, we introduce modern tools like the DoWhy framework, which streamline the process of modeling causal relationships and validating causal estimates.<\/p>\n\n\n\n

<\/div>\n\n\n\n

The Event will also be available online for remote participants at this link:<\/strong> www.teams.microsoft.com<\/a><\/p>\n","protected":false},"featured_media":5331,"template":"","meta":[],"event_listing_category":[],"event_listing_type":[],"_links":{"self":[{"href":"https:\/\/www.hpc.mk\/index.php\/wp-json\/wp\/v2\/event_listing\/5236"}],"collection":[{"href":"https:\/\/www.hpc.mk\/index.php\/wp-json\/wp\/v2\/event_listing"}],"about":[{"href":"https:\/\/www.hpc.mk\/index.php\/wp-json\/wp\/v2\/types\/event_listing"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.hpc.mk\/index.php\/wp-json\/wp\/v2\/media\/5331"}],"wp:attachment":[{"href":"https:\/\/www.hpc.mk\/index.php\/wp-json\/wp\/v2\/media?parent=5236"}],"wp:term":[{"taxonomy":"event_listing_category","embeddable":true,"href":"https:\/\/www.hpc.mk\/index.php\/wp-json\/wp\/v2\/event_listing_category?post=5236"},{"taxonomy":"event_listing_type","embeddable":true,"href":"https:\/\/www.hpc.mk\/index.php\/wp-json\/wp\/v2\/event_listing_type?post=5236"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}