{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110479"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110479","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Time series forecasting with recurrent neural networks","abstract":"Time series, such as demand trends, stock prices, and sensor data, is an essential data type in our modern world. Over the years, many models such as Exponential Smoothing and ARIMA are developed to make forecasts on time series. Recently, Recurrent Neural Networks (RNN) is gaining traction in the field of time series forecasting. RNN is a type of specialized neural network tailored towards handling sequential data such as natural language and time series. RNN models such as LSTM networks and GRU networks are widely used in literature. Besides, different feature engineering methods such as CEEMDAN are also tools employed in the literature to improve prediction accuracy. In this paper, we will introduce different models and methods of handling time series and will conduct a comparative case study using the S$\\&$P500 index to compare the effectiveness of these models.","abstract_html":"Time series, such as demand trends, stock prices, and sensor data, is an essential data type in our modern world. Over the years, many models such as Exponential Smoothing and ARIMA are developed to make forecasts on time series. Recently, Recurrent Neural Networks (RNN) is gaining traction in the field of time series forecasting. RNN is a type of specialized neural network tailored towards handling sequential data such as natural language and time series. RNN models such as LSTM networks and GRU networks are widely used in literature. Besides, different feature engineering methods such as CEEMDAN are also tools employed in the literature to improve prediction accuracy. In this paper, we will introduce different models and methods of handling time series and will conduct a comparative case study using the S$\\&$P500 index to compare the effectiveness of these models.","abstract_has_math":true,"creators":["Pan, Zhonghao"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Kim, Harrison M"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T01:10:57Z","date_published":"2021-09-17T01:10:57Z","updated_at":"2026-07-22T22:24:50Z","subjects":["Time Series","Recurrent Neural Networks","LSTM","GRU","CEEMDAN"],"languages":["en"],"rights":["Copyright 2021 Zhonghao Pan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110479","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kim, Harrison M"]},{"key":"dc:creator","label":"Author","values":["Pan, Zhonghao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T01:10:57Z","2021-04-18","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial Engineering"]},{"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":["Time Series","Recurrent Neural Networks","LSTM","GRU","CEEMDAN"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Zhonghao Pan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110479"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Time series, such as demand trends, stock prices, and sensor data, is an essential data type in our modern world. Over the years, many models such as Exponential Smoothing and ARIMA are developed to make forecasts on time series. Recently, Recurrent Neural Networks (RNN) is gaining traction in the field of time series forecasting. RNN is a type of specialized neural network tailored towards handling sequential data such as natural language and time series. RNN models such as LSTM networks and GRU networks are widely used in literature. Besides, different feature engineering methods such as CEEMDAN are also tools employed in the literature to improve prediction accuracy. In this paper, we will introduce different models and methods of handling time series and will conduct a comparative case study using the S$\\&$P500 index to compare the effectiveness of these models.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Zhonghao Pan, accepted the attached license on 2021-04-15 at 09:40.","The student, Zhonghao Pan, submitted this Thesis for approval on 2021-04-15 at 09:44.","This Thesis was approved for publication on 2021-04-18 at 15:12.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16342 on 2021-09-16 at 16:41:52","Made available in DSpace on 2021-09-17T01:10:57Z (GMT). No. of bitstreams: 2 PAN-THESIS-2021.pdf: 1373665 bytes, checksum: 08ef0d8b0118bc19c3e85b3633f705c5 (MD5) LICENSE.txt: 4205 bytes, checksum: 89ea62c9e1e21631bc60c28074f18a53 (MD5) Previous issue date: 2021-04-18"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Time series forecasting with recurrent neural networks"]}]}],"canonical_facts":{"dc:contributor":["Kim, Harrison M"],"dc:creator":["Pan, Zhonghao"],"dc:date":["2021-09-17T01:10:57Z","2021-04-18","2021-05"],"dc:description":["Time series, such as demand trends, stock prices, and sensor data, is an essential data type in our modern world. Over the years, many models such as Exponential Smoothing and ARIMA are developed to make forecasts on time series. Recently, Recurrent Neural Networks (RNN) is gaining traction in the field of time series forecasting. RNN is a type of specialized neural network tailored towards handling sequential data such as natural language and time series. RNN models such as LSTM networks and GRU networks are widely used in literature. Besides, different feature engineering methods such as CEEMDAN are also tools employed in the literature to improve prediction accuracy. In this paper, we will introduce different models and methods of handling time series and will conduct a comparative case study using the S$\\&$P500 index to compare the effectiveness of these models.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Zhonghao Pan, accepted the attached license on 2021-04-15 at 09:40.","The student, Zhonghao Pan, submitted this Thesis for approval on 2021-04-15 at 09:44.","This Thesis was approved for publication on 2021-04-18 at 15:12.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16342 on 2021-09-16 at 16:41:52","Made available in DSpace on 2021-09-17T01:10:57Z (GMT). No. of bitstreams: 2 PAN-THESIS-2021.pdf: 1373665 bytes, checksum: 08ef0d8b0118bc19c3e85b3633f705c5 (MD5) LICENSE.txt: 4205 bytes, checksum: 89ea62c9e1e21631bc60c28074f18a53 (MD5) Previous issue date: 2021-04-18"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/110479"],"dc:language":["en"],"dc:rights":["Copyright 2021 Zhonghao Pan"],"dc:subject":["Time Series","Recurrent Neural Networks","LSTM","GRU","CEEMDAN"],"dc:title":["Time series forecasting with recurrent neural networks"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Industrial Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:50Z"}