{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90834"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90834","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Optimizing smoothing parameters for the triple exponential forecasting model","abstract":"Exponential smoothing has always been a popular topic of research in forecasting. The triple exponential smoothing in particular involves modeling a function that is a combination of level, trend and seasonal factors. While simulating the model, each of the factors is associated with a parameter whose value has a significant impact on the accuracy of the forecast, yet optimizing these parameters for a time series has received relatively little attention in literature. In this thesis we will explore the results of multi-step forecasting by using parameters optimized through an algorithm centered around h-step ahead errors. An empirical study conducted on forecasting the monthly time series from the M3-Competition across a range of horizons gave us promising results. We show that this method proves to be better than the standard Holt-Winters procedure for the entire forecasting horizon in five out the six categories of data considered . We also show that this method significantly improves the accuracy over the short term forecasting horizon when compared to the automated Holt-Winters procedure used by experts in the M3 competition. Encouraged by these results, we recommend replicating this methodology to other models of the triple exponential smoothing in the future.","abstract_html":"Exponential smoothing has always been a popular topic of research in forecasting. The triple exponential smoothing in particular involves modeling a function that is a combination of level, trend and seasonal factors. While simulating the model, each of the factors is associated with a parameter whose value has a significant impact on the accuracy of the forecast, yet optimizing these parameters for a time series has received relatively little attention in literature. In this thesis we will explore the results of multi-step forecasting by using parameters optimized through an algorithm centered around h-step ahead errors. An empirical study conducted on forecasting the monthly time series from the M3-Competition across a range of horizons gave us promising results. We show that this method proves to be better than the standard Holt-Winters procedure for the entire forecasting horizon in five out the six categories of data considered . We also show that this method significantly improves the accuracy over the short term forecasting horizon when compared to the automated Holt-Winters procedure used by experts in the M3 competition. Encouraged by these results, we recommend replicating this methodology to other models of the triple exponential smoothing in the future.","abstract_has_math":false,"creators":["Narasingaraj, Harish Balaji"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Nagi, Rakesh"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-07-07T20:35:18Z","date_published":"2016-07-07T20:35:18Z","updated_at":"2026-07-22T22:26:34Z","subjects":["Holt Winters","Triple Exponential Smoothing parameters","M3 Competition"],"languages":["en"],"rights":["Copyright 2016 Harish B Narasingaraj"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/90834","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Nagi, Rakesh"]},{"key":"dc:creator","label":"Author","values":["Narasingaraj, Harish Balaji"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-07-07T20:35:18Z","2018-07-08T09:15:30Z","2016-04-26","2016-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"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":["Holt Winters","Triple Exponential Smoothing parameters","M3 Competition"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Harish B Narasingaraj"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/90834"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Exponential smoothing has always been a popular topic of research in forecasting. The triple exponential smoothing in particular involves modeling a function that is a combination of level, trend and seasonal factors. While simulating the model, each of the factors is associated with a parameter whose value has a significant impact on the accuracy of the forecast, yet optimizing these parameters for a time series has received relatively little attention in literature. In this thesis we will explore the results of multi-step forecasting by using parameters optimized through an algorithm centered around h-step ahead errors. An empirical study conducted on forecasting the monthly time series from the M3-Competition across a range of horizons gave us promising results. We show that this method proves to be better than the standard Holt-Winters procedure for the entire forecasting horizon in five out the six categories of data considered . We also show that this method significantly improves the accuracy over the short term forecasting horizon when compared to the automated Holt-Winters procedure used by experts in the M3 competition. Encouraged by these results, we recommend replicating this methodology to other models of the triple exponential smoothing in the future.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Harish Narasingaraj, accepted the attached license on 2016-04-26 at 10:50.","The student, Harish Narasingaraj, submitted this Thesis for approval on 2016-04-26 at 10:58.","This Thesis was approved for publication on 2016-04-26 at 14:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9501 on 2016-07-07 at 13:50:56","Made available in DSpace on 2016-07-07T20:35:18Z (GMT). No. of bitstreams: 2 NARASINGARAJ-THESIS-2016.pdf: 580724 bytes, checksum: be7968910493c4eb9225fbf1c116874a (MD5) LICENSE.txt: 4216 bytes, checksum: 2185be821ce7a01f6cbc4b01d535a446 (MD5) Previous issue date: 2016-04-26","Embargo set by: Seth Robbins for item 93187 Lift date: 2018-07-07T20:35:34Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 93187 on 2018-07-08T09:15:30Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Optimizing smoothing parameters for the triple exponential forecasting model"]}]}],"canonical_facts":{"dc:contributor":["Nagi, Rakesh"],"dc:creator":["Narasingaraj, Harish Balaji"],"dc:date":["2016-07-07T20:35:18Z","2018-07-08T09:15:30Z","2016-04-26","2016-05"],"dc:description":["Exponential smoothing has always been a popular topic of research in forecasting. The triple exponential smoothing in particular involves modeling a function that is a combination of level, trend and seasonal factors. While simulating the model, each of the factors is associated with a parameter whose value has a significant impact on the accuracy of the forecast, yet optimizing these parameters for a time series has received relatively little attention in literature. In this thesis we will explore the results of multi-step forecasting by using parameters optimized through an algorithm centered around h-step ahead errors. An empirical study conducted on forecasting the monthly time series from the M3-Competition across a range of horizons gave us promising results. We show that this method proves to be better than the standard Holt-Winters procedure for the entire forecasting horizon in five out the six categories of data considered . We also show that this method significantly improves the accuracy over the short term forecasting horizon when compared to the automated Holt-Winters procedure used by experts in the M3 competition. Encouraged by these results, we recommend replicating this methodology to other models of the triple exponential smoothing in the future.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Harish Narasingaraj, accepted the attached license on 2016-04-26 at 10:50.","The student, Harish Narasingaraj, submitted this Thesis for approval on 2016-04-26 at 10:58.","This Thesis was approved for publication on 2016-04-26 at 14:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9501 on 2016-07-07 at 13:50:56","Made available in DSpace on 2016-07-07T20:35:18Z (GMT). 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