{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/121349"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/121349","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Enhancing deep state space models for complex applications","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-08-01","abstract_has_math":false,"creators":["Nagda, Chandni"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Banerjee, Arindam"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-08","date_published":"2023-08","updated_at":"2026-07-22T22:24:57Z","subjects":["Machine Learning","Deep Learning","State Space Models","Multivariate Time Series"],"languages":["en","eng"],"rights":["Copyright 2023 Chandni Nagda"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/121349","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Banerjee, Arindam"]},{"key":"dc:creator","label":"Author","values":["Nagda, Chandni"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-08","2023-07-17"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning","Deep Learning","State Space Models","Multivariate Time Series"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Chandni Nagda"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/121349"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-08-01","The student, Chandni Nagda, accepted the attached license on 2023-07-10 at 13:40.","The student, Chandni Nagda, submitted this Thesis for approval on 2023-07-10 at 13:46.","This Thesis was approved for publication on 2023-07-17 at 16:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19600 on 2023-12-04 at 17:18:27","This thesis investigates the potential to improve deep state space models towards their use in applications such as the prediction and simulation of tropical cyclone (TC) tracks. Given the significant implications of increasingly extreme and unpredictable TCs due to anthropogenic climate change, there is a pressing need for more accurate and rapid forecasting models that can adapt to changing climate patterns. Current forecasting methods, either computationally intensive dynamical models or oversimplified statistical models reliant on historical data, struggle to meet these demands. In response, this work makes three significant contributions. First, it explores overparameterization in deep state space models, specifically the effects of width scaling. Preliminary findings indicate that the width of transition and emission networks can be increased to improve model performance, with the caveat of potential overfitting risks for small datasets. Second, we propose a new deep state space model that employs hierarchical latent states to enhance model expressiveness. Lastly, we present an application of deep state space models to the task of TC track simulation. While the model was able to generate small-scale movements, it struggled to capture overall trends observed in cyclones, indicating a need for improved deep state space models for climate applications. This thesis illuminates the promise and challenges of applying deep state space models to TC forecasting and simulation. It paves the way for future research aiming to develop a deep learning-based model capable of generating tropical cyclones under projected climate scenarios, a critical tool for risk assessment, hazard prediction, and policy decision-making amidst the growing uncertainties of climate change."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Enhancing deep state space models for complex applications"]}]}],"canonical_facts":{"dc:contributor":["Banerjee, Arindam"],"dc:creator":["Nagda, Chandni"],"dc:date":["2023-08","2023-07-17"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-08-01","The student, Chandni Nagda, accepted the attached license on 2023-07-10 at 13:40.","The student, Chandni Nagda, submitted this Thesis for approval on 2023-07-10 at 13:46.","This Thesis was approved for publication on 2023-07-17 at 16:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19600 on 2023-12-04 at 17:18:27","This thesis investigates the potential to improve deep state space models towards their use in applications such as the prediction and simulation of tropical cyclone (TC) tracks. Given the significant implications of increasingly extreme and unpredictable TCs due to anthropogenic climate change, there is a pressing need for more accurate and rapid forecasting models that can adapt to changing climate patterns. Current forecasting methods, either computationally intensive dynamical models or oversimplified statistical models reliant on historical data, struggle to meet these demands. In response, this work makes three significant contributions. First, it explores overparameterization in deep state space models, specifically the effects of width scaling. Preliminary findings indicate that the width of transition and emission networks can be increased to improve model performance, with the caveat of potential overfitting risks for small datasets. Second, we propose a new deep state space model that employs hierarchical latent states to enhance model expressiveness. Lastly, we present an application of deep state space models to the task of TC track simulation. While the model was able to generate small-scale movements, it struggled to capture overall trends observed in cyclones, indicating a need for improved deep state space models for climate applications. This thesis illuminates the promise and challenges of applying deep state space models to TC forecasting and simulation. It paves the way for future research aiming to develop a deep learning-based model capable of generating tropical cyclones under projected climate scenarios, a critical tool for risk assessment, hazard prediction, and policy decision-making amidst the growing uncertainties of climate change."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/121349"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Chandni Nagda"],"dc:subject":["Machine Learning","Deep Learning","State Space Models","Multivariate Time Series"],"dc:title":["Enhancing deep state space models for complex applications"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}