{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T21:29:05Z","timestamp":1777411745606,"version":"3.51.4"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030923099","type":"print"},{"value":"9783030923105","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-92310-5_68","type":"book-chapter","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T14:04:20Z","timestamp":1638799460000},"page":"587-596","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An Improved Time-Series Forecasting Model Using Time Series Decomposition and GRU Architecture"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6865-9598","authenticated-orcid":false,"given":"Hyun Jae","family":"Jo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3353-8515","authenticated-orcid":false,"given":"Won Joong","family":"Kim","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2952-5540","authenticated-orcid":false,"given":"Hyo Kyung","family":"Goh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0911-7347","authenticated-orcid":false,"given":"Chi-Hyuck","family":"Jun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,2]]},"reference":[{"issue":"4","key":"68_CR1","doi-asserted-by":"publisher","first-page":"1586","DOI":"10.1109\/TNNLS.2020.2985720","volume":"32","author":"K Bandara","year":"2020","unstructured":"Bandara, K., Bergmeir, C., Hewamalage, H.: LSTM-MSNet: leveraging forecasts on sets of related time series with multiple seasonal patterns. IEEE Trans. Neural Netw. Learn. Syst. 32(4), 1586\u20131599 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"2","key":"68_CR2","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157\u2013166 (1994)","journal-title":"IEEE Trans. Neural Netw."},{"key":"68_CR3","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"68_CR4","unstructured":"Chung, J., et al.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)"},{"issue":"1","key":"68_CR5","first-page":"3","volume":"6","author":"RB Cleveland","year":"1990","unstructured":"Cleveland, R.B., et al.: STL: a seasonal-trend decomposition. J. Off. Stat. 6(1), 3\u201373 (1990)","journal-title":"J. Off. Stat."},{"issue":"496","key":"68_CR6","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1198\/jasa.2011.tm09771","volume":"106","author":"AM De Livera","year":"2011","unstructured":"De Livera, A.M., Hyndman, R.J., Snyder, R.D.: Forecasting time series with complex seasonal patterns using exponential smoothing. J. Am. Stat. Assoc. 106(496), 1513\u20131527 (2011)","journal-title":"J. Am. Stat. Assoc."},{"key":"68_CR7","doi-asserted-by":"crossref","unstructured":"Dokumentov, A., Hyndman, R.J.: STR: a seasonal-trend decomposition procedure based on regression. arXiv preprint arXiv:2009.05894 (2020)","DOI":"10.1287\/ijds.2021.0004"},{"issue":"8","key":"68_CR8","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"68_CR9","doi-asserted-by":"crossref","unstructured":"Huo, Y., et al.: Long-term span passengers prediction model based on STL decomposition and LSTM. In: 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1\u20134. IEEE (2019)","DOI":"10.23919\/APNOMS.2019.8892991"},{"key":"68_CR10","doi-asserted-by":"publisher","unstructured":"M\u00e9ndez-Jim\u00e9nez, I., C\u00e1rdenas-Montes, M.: Time series decomposition for improving the forecasting performance of convolutional neural networks. In: Herrera, F., et al. (eds.) CAEPIA 2018. LNCS (LNAI), vol. 11160, pp. 87\u201397. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00374-6_9","DOI":"10.1007\/978-3-030-00374-6_9"},{"key":"68_CR11","doi-asserted-by":"crossref","unstructured":"Sebastian, K., Gao, H., Xing, X.: Utilizing an ensemble STL decomposition and GRU model for base station traffic forecasting. In: 2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pp. 314\u2013319. IEEE (2020)","DOI":"10.23919\/SICE48898.2020.9240357"},{"key":"68_CR12","unstructured":"Shiskin, J.: The X-11 variant of the census method II seasonal adjustment program. No. 15. US Department of Commerce, Bureau of the Census (1967)"},{"key":"68_CR13","doi-asserted-by":"crossref","unstructured":"Peng, W.: DLI: a deep learning-based granger causality inference. Complexity 2020, article ID 5960171, 6 p. (2020)","DOI":"10.1155\/2020\/5960171"},{"issue":"12","key":"68_CR14","doi-asserted-by":"publisher","first-page":"612","DOI":"10.3390\/agriculture10120612","volume":"10","author":"H Yin","year":"2020","unstructured":"Yin, H., et al.: STL-ATTLSTM: vegetable price forecasting using STL and attention mechanism-based LSTM. Agriculture 10(12), 612 (2020)","journal-title":"Agriculture"},{"key":"68_CR15","doi-asserted-by":"publisher","unstructured":"Zhang, X., Shen, F., Zhao, J., Yang, G.H.: Time series forecasting using GRU neural network with multi-lag after decomposition. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.-S.M. (eds.) ICONIP 2017. LNCS, vol. 10638, pp. 523\u2013532. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-70139-4_53","DOI":"10.1007\/978-3-319-70139-4_53"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92310-5_68","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T08:13:39Z","timestamp":1655799219000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92310-5_68"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030923099","9783030923105"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92310-5_68","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"2 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sanur, Bali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2021.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1093","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"226","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"177","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.57","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was held online.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}