{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:47:48Z","timestamp":1791928068785,"version":"4.3.4"},"reference-count":21,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100019786","name":"Universitas Negeri Semarang","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100019786","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["MethodsX"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.mex.2026.104169","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T06:42:58Z","timestamp":1789627378000},"page":"104169","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Multichannel SSA decomposition in a hybrid SSA\u2013N-BEATS framework for complex time series forecasting"],"prefix":"10.1016","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0661-575X","authenticated-orcid":false,"given":"Alfian Adi","family":"Pratama","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7289-8708","authenticated-orcid":false,"given":"Putriaji","family":"Hendikawati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.mex.2026.104169_bib0001","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.asoc.2018.01.017","article-title":"A novel decomposition-ensemble model for forecasting short-term load-time series with multiple seasonal patterns","volume":"65","author":"Zhang","year":"2018","journal-title":"Appl. Soft. Comput. J."},{"key":"10.1016\/j.mex.2026.104169_bib0002","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1109\/OAJPE.2020.3029979","article-title":"Energy forecasting: a review and outlook","volume":"7","author":"Hong","year":"2020","journal-title":"IEEE Open Access J. Power Energy"},{"key":"10.1016\/j.mex.2026.104169_bib0003","doi-asserted-by":"crossref","first-page":"4406","DOI":"10.3390\/en16114406","article-title":"A comprehensive review of deep-learning applications to power quality analysis","volume":"16","author":"Samanta","year":"2023","journal-title":"Energies"},{"key":"10.1016\/j.mex.2026.104169_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2025.126197","article-title":"Novel deep hybrid model for electricity price prediction based on dual decomposition","volume":"395","author":"Ghimire","year":"2025","journal-title":"Appl. Energy"},{"key":"10.1016\/j.mex.2026.104169_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.mex.2020.101015","article-title":"Time series forecasting using singular spectrum analysis, fuzzy systems and neural networks","volume":"7","author":"Sulandari","year":"2020","journal-title":"MethodsX"},{"key":"10.1016\/j.mex.2026.104169_bib0006","author":"Golyandina","year":"2001","journal-title":"Anal. Time Ser. Struct.: SSA Relat. Tech."},{"key":"10.1016\/j.mex.2026.104169_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2021.116918","article-title":"N-BEATS neural network for mid-term electricity load forecasting","volume":"293","author":"Oreshkin","year":"2021","journal-title":"Appl. Energy"},{"key":"10.1016\/j.mex.2026.104169_bib0008","series-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"2623","article-title":"Optuna: a next-generation hyperparameter optimization framework","author":"Akiba","year":"2019"},{"key":"10.1016\/j.mex.2026.104169_bib0009","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","article-title":"On hyperparameter optimization of machine learning algorithms: theory and practice","volume":"415","author":"Yang","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.mex.2026.104169_bib0010","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.ijforecast.2020.06.008","article-title":"Recurrent neural networks for time series forecasting: current status and future directions","volume":"37","author":"Hewamalage","year":"2021","journal-title":"Int. J. Forecast"},{"key":"10.1016\/j.mex.2026.104169_bib0011","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.623","article-title":"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation","volume":"7","author":"Chicco","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"10.1016\/j.mex.2026.104169_bib0012","unstructured":"B.N. Oreshkin, D. Carpov, N. Chapados, Y. Bengio, N-BEATS: neural basis expansion analysis for interpretable time series forecasting, 2020. http:\/\/arxiv.org\/abs\/1905.10437."},{"key":"10.1016\/j.mex.2026.104169_bib0013","unstructured":"F. Lopes, D. Gibert, V. Courtillot, J.-L. Le Mou\u00ebl, J.-B. Boul\u00e9, On the optimization of singular spectrum analyses: a pragmatic approach, 2024. http:\/\/arxiv.org\/abs\/2412.17793."},{"key":"10.1016\/j.mex.2026.104169_bib0014","unstructured":"T. Knapik, A. Ratiarison, H. Razafindralambo, An experimental evaluation of choices of SSA forecasting parameters, 2024. https:\/\/hal.science\/hal-03679576v1."},{"key":"10.1016\/j.mex.2026.104169_bib0015","doi-asserted-by":"crossref","unstructured":"N. Golyandina, Particularities and commonalities of singular spectrum analysis as a method of time series analysis and signal processing, 2020. https:\/\/doi.org\/10.1002\/wics.1487.","DOI":"10.1002\/wics.1487"},{"key":"10.1016\/j.mex.2026.104169_bib0016","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1007\/s00521-020-05113-0","article-title":"A combined model based on SSA, neural networks, and LSSVM for short-term electric load and price forecasting","volume":"33","author":"Zhang","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.mex.2026.104169_bib0017","doi-asserted-by":"crossref","first-page":"73736","DOI":"10.1109\/ACCESS.2021.3078900","article-title":"A novel short-term load forecasting method by combining the deep learning with singular spectrum analysis","volume":"9","author":"Pham","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.mex.2026.104169_bib0018","doi-asserted-by":"crossref","first-page":"8128","DOI":"10.3390\/en15218128","article-title":"Energy price prediction integrated with singular spectrum analysis and long short-term memory network against the background of carbon neutrality","volume":"15","author":"Zhu","year":"2022","journal-title":"Energies"},{"key":"10.1016\/j.mex.2026.104169_bib0019","first-page":"35","article-title":"Seasonal decomposition and trend using hybrid STL-FNN with application","volume":"7","author":"Sultan","year":"2023","journal-title":"Adv. Theor. Nonlinear Anal. Appl."},{"key":"10.1016\/j.mex.2026.104169_bib0020","doi-asserted-by":"crossref","first-page":"5517","DOI":"10.3390\/app15105517","article-title":"A hybrid deep learning model based on FFT-STL decomposition for ocean wave height prediction","volume":"15","author":"Sun","year":"2025","journal-title":"Appl. Sci."},{"key":"10.1016\/j.mex.2026.104169_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.131407","article-title":"STNet: seasonal-trend network for multivariate time series forecasting","volume":"655","author":"Li","year":"2025","journal-title":"Neurocomputing"}],"container-title":["MethodsX"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2215016126003857?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2215016126003857?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:24:34Z","timestamp":1791926674000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2215016126003857"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":21,"alternative-id":["S2215016126003857"],"URL":"https:\/\/doi.org\/10.1016\/j.mex.2026.104169","relation":{},"ISSN":["2215-0161"],"issn-type":[{"value":"2215-0161","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multichannel SSA decomposition in a hybrid SSA\u2013N-BEATS framework for complex time series forecasting","name":"articletitle","label":"Article Title"},{"value":"MethodsX","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.mex.2026.104169","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"104169"}}