{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T18:40:07Z","timestamp":1772563207271,"version":"3.50.1"},"reference-count":38,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"8","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1109\/tai.2024.3365775","type":"journal-article","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T15:25:18Z","timestamp":1708356318000},"page":"4258-4268","source":"Crossref","is-referenced-by-count":3,"title":["Dynamic Long-Term Time-Series Forecasting via Meta Transformer Networks"],"prefix":"10.1109","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9251-7781","authenticated-orcid":false,"given":"Muhammad Anwar","family":"Ma'sum","sequence":"first","affiliation":[{"name":"STEM, University of South Australia, Adelaide, SA, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0516-8593","authenticated-orcid":false,"given":"MD Rasel","family":"Sarkar","sequence":"additional","affiliation":[{"name":"SEIT, University of New South Wales, Canberra, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6531-5087","authenticated-orcid":false,"given":"Mahardhika","family":"Pratama","sequence":"additional","affiliation":[{"name":"STEM, University of South Australia, Adelaide, SA, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1534-2989","authenticated-orcid":false,"given":"Savitha","family":"Ramasamy","sequence":"additional","affiliation":[{"name":"I2R, A&#x002A;Star, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4754-8191","authenticated-orcid":false,"given":"Sreenatha","family":"Anavatti","sequence":"additional","affiliation":[{"name":"SEIT, University of New South Wales, Canberra, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2843-5738","authenticated-orcid":false,"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"STEM, University of South Australia, Adelaide, SA, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9542-9525","authenticated-orcid":false,"given":"Habibullah","family":"Habibullah","sequence":"additional","affiliation":[{"name":"STEM, University of South Australia, Adelaide, SA, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0937-4028","authenticated-orcid":false,"given":"Ryszard","family":"Kowalczyk","sequence":"additional","affiliation":[{"name":"STEM, University of South Australia, Adelaide, SA, Australia"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Online incremental feature learning with denoising autoencoders","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Zhou","year":"2012"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/369"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00021"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357946"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.75"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.ins.2021.04.041","article-title":"Online bagging of evolving fuzzy systems","volume":"570","author":"Lughofer","year":"2021","journal-title":"Inf. Sci."},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2018.2796099"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2019.2939993"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71246-8_29"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86486-6_25"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/324"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref13","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Wu","year":"2021"},{"key":"ref14","article-title":"DualNet: Continual learning, fast and slow","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Pham","year":"2021"},{"key":"ref15","article-title":"A universal representation transformer layer for few-shot image classification","author":"Liu","year":"2021"},{"key":"ref16","article-title":"Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting","author":"Zhou","year":"2022"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539329"},{"key":"ref19","article-title":"Barlow twins: Self-supervised learning via redundancy reduction","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zbontar","year":"2021"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"265","DOI":"10.2307\/2988198","article-title":"Time series analysis: Forecasting and control","volume":"27","author":"Box","year":"1978","journal-title":"Statistician"},{"key":"ref21","article-title":"Normalizing Kalman filters for multivariate time series analysis","volume-title":"Proc. Neural Inf. Process. Syst.","author":"de B\u00e9zenac","year":"2020"},{"key":"ref22","article-title":"A multi-horizon quantile recurrent forecaster","author":"Wen","year":"2017","journal-title":"Mach. Learn."},{"key":"ref23","article-title":"Deep state space models for time series forecasting","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Rangapuram","year":"2018"},{"key":"ref24","article-title":"DeepAR: Probabilistic forecasting with autoregressive recurrent networks","author":"Flunkert","year":"2017"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210006"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11635"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/366"},{"key":"ref28","article-title":"Conditional time series forecasting with convolutional neural networks","volume-title":"Mach. Learn.","author":"Borovykh","year":"2017"},{"key":"ref29","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018"},{"key":"ref30","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"ref31","article-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","author":"Li","year":"2019"},{"key":"ref32","article-title":"Reformer: The efficient transformer","author":"Kitaev","year":"2020"},{"key":"ref33","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"Finn","year":"2017","journal-title":"Proc. Int. Conf. Mach. Learn."},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482271"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/2523813"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467401"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11671"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9078688\/10635096\/10439265.pdf?arnumber=10439265","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T01:09:16Z","timestamp":1755911356000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10439265\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":38,"journal-issue":{"issue":"8"},"URL":"https:\/\/doi.org\/10.1109\/tai.2024.3365775","relation":{},"ISSN":["2691-4581"],"issn-type":[{"value":"2691-4581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8]]}}}