{"678838":{"#nid":"678838","#data":{"type":"event","title":"PhD Defense by Hanyu Zhang ","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u0026nbsp;Advancing Time Series Forecasting: Hierarchical Methods, Probabilistic Models, and Domain Knowledge Integration from Power Systems to Retail\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E\u0026nbsp;Dec 17th, 2024\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E\u0026nbsp;8 AM - 10 AM\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ELocation:\u003C\/strong\u003E\u0026nbsp;CODA 1215, \u003Ca href=\u0022https:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_NDY4YzFhMTMtMjBhMi00NTdkLTg0OTEtOWY5OGI5YjM0OWM4%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%2285b3a23a-6b6d-4161-9ffa-669984431950%22%7d\u0022 title=\u0022https:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_NDY4YzFhMTMtMjBhMi00NTdkLTg0OTEtOWY5OGI5YjM0OWM4%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%2285b3a23a-6b6d-4161-9ffa-669984431950%22%7d\u0022\u003Emeeting link\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EHanyu Zhang\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EMachine Learning PhD Student\u003C\/p\u003E\u003Cp\u003EH. Milton Stewart School of Industrial and Systems Engineering\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Pascal Van Hentenryck (Advisor), H. Milton Stewart School of Industrial and Systems Engineering\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Yao Xie, H. Milton Stewart School of Industrial and Systems Engineering\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Siva Theja Maguluri, H. Milton Stewart School of Industrial and Systems Engineering\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. B. Aditya Prakash, School of Computational Science and Engineering\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Terrence Mak, Department of Data Science \u0026amp; AI, Monash University\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E This thesis advances time series forecasting through three novel methodological contributions addressing key challenges in modern forecasting applications. First, it introduces the Bundle-Predict-Reconcile (BPR) framework, which improves hierarchical wind power forecasting by learning optimal groupings of related time series while maintaining forecast consistency across different levels. Second, it develops a weather-informed probabilistic forecasting framework that combines Temporal Fusion Transformers with Gaussian copula methods to capture spatio-temporal dependencies in renewable energy systems. Finally, it presents LLMForecaster, an innovative approach that leverages large language models to incorporate unstructured textual information into time series forecasts, significantly improving accuracy for products with seasonal patterns. The proposed methods are extensively validated on real-world datasets from power systems and retail domains, demonstrating substantial improvements over existing approaches in forecast accuracy and uncertainty quantification.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAdvancing Time Series Forecasting: Hierarchical Methods, Probabilistic Models, and Domain Knowledge Integration from Power Systems to Retail\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Advancing Time Series Forecasting: Hierarchical Methods, Probabilistic Models, and Domain Knowledge Integration from Power Systems to Retail"}],"uid":"27707","created_gmt":"2024-12-11 17:20:41","changed_gmt":"2024-12-11 17:21:27","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-12-17T09:00:00-05:00","event_time_end":"2024-12-17T10:00:00-05:00","event_time_end_last":"2024-12-17T10:00:00-05:00","gmt_time_start":"2024-12-17 14:00:00","gmt_time_end":"2024-12-17 15:00:00","gmt_time_end_last":"2024-12-17 15:00:00","rrule":null,"timezone":"America\/New_York"},"location":"CODA 1215","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}