{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T07:32:35Z","timestamp":1763191955374,"version":"3.45.0"},"reference-count":33,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"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":[],"published-print":{"date-parts":[[2025,6,30]]},"DOI":"10.1109\/ijcnn64981.2025.11228652","type":"proceedings-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T18:46:15Z","timestamp":1763145975000},"page":"1-10","source":"Crossref","is-referenced-by-count":0,"title":["Tailored Architectures for Time Series Forecasting: Evaluating Deep Learning Models on Gaussian Process-Generated Data"],"prefix":"10.1109","author":[{"given":"Victoria","family":"Hankemeier","sequence":"first","affiliation":[{"name":"University of M&#x00FC;nster,Autonomous Intelligent Systems Group,M&#x00FC;nster,Germany"}]},{"given":"Malte","family":"Schilling","sequence":"additional","affiliation":[{"name":"University of M&#x00FC;nster,Autonomous Intelligent Systems Group,M&#x00FC;nster,Germany"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"article-title":"Revisiting long-term time series forecasting: An investigation on linear mapping","year":"2023","author":"Li","key":"ref2"},{"article-title":"Wave-mask\/mix: Exploring wavelet-based augmentations for time series forecasting","year":"2024","author":"Arabi","key":"ref3"},{"key":"ref4","article-title":"Timemixer: Decomposable multiscale mixing for time series forecasting","volume":"abs\/2405.14616","author":"Wang","year":"2024","journal-title":"CoRR"},{"key":"ref5","article-title":"Timesnet: Temporal 2d-variation modeling for general time series analysis","author":"Wu","year":"2023","journal-title":"ICLR"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1406.1078"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-08010-9_33"},{"article-title":"Wavenet: A generative model for raw audio","year":"2016","author":"van den Oord","key":"ref9"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.113"},{"key":"ref11","article-title":"Adawavenet: Adaptive wavelet network for time series analysis","author":"Yu","year":"2024","journal-title":"Transactions on Machine Learning Research"},{"key":"ref12","article-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","volume":"32","author":"Li","year":"2019","journal-title":"NeurIPS"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref14","article-title":"Autoformer: decomposition transformers with auto-correlation for long-term series forecasting","author":"Wu","year":"2021","journal-title":"NeurIPS"},{"key":"ref15","first-page":"27268","article-title":"FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting","author":"Zhou","year":"2022","journal-title":"ICML"},{"key":"ref16","article-title":"Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting","author":"Liu","year":"2022","journal-title":"ICLR"},{"key":"ref17","article-title":"Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting","author":"Zhang","year":"2023","journal-title":"ICLR"},{"key":"ref18","article-title":"A time series is worth 64 words: Long-term forecasting with transformers","author":"Nie","year":"2023","journal-title":"ICLR"},{"key":"ref19","article-title":"itransformer: Inverted transformers are effective for time series forecasting","author":"Liu","year":"2024","journal-title":"ICLR"},{"key":"ref20","first-page":"5335","article-title":"Vcformer: Variable correlation transformer with inherent lagged correlation for multivariate time series forecasting","author":"Yang","year":"2024","journal-title":"IJCAI"},{"key":"ref21","article-title":"Timexer: Empowering transformers for time series forecasting with exogenous variables","author":"Wang","year":"2024","journal-title":"NeurIPS"},{"article-title":"Mamba: Linear-time sequence modeling with selective state spaces","year":"2024","author":"Gu","key":"ref22"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3233\/FAIA240677"},{"key":"ref24","article-title":"Longterm forecasting with tiDE: Time-series dense encoder","author":"Das","year":"2023","journal-title":"Transactions on Machine Learning Research"},{"key":"ref25","article-title":"Sparsetsf: Modeling long-term time series forecasting with 1k parameters","author":"Lin","year":"2024","journal-title":"ICML"},{"key":"ref26","article-title":"Cyclenet: Enhancing time series forecasting through modeling periodic patterns","author":"Lin","year":"2024","journal-title":"NeurIPS"},{"article-title":"Mamba or transformer for time series forecasting? mixture of universals (mou) is all you need","year":"2024","author":"Peng","key":"ref27"},{"key":"ref28","article-title":"Onenet: Enhancing time series forecasting models under concept drift by online ensembling","author":"Zhang","year":"2023","journal-title":"NeurIPS"},{"article-title":"Deep time series models: A comprehensive survey and benchmark","year":"2024","author":"Wang","key":"ref29"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.14778\/3665844.3665863"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref32","article-title":"Reversible instance normalization for accurate time-series forecasting against distribution shift","author":"Kim","year":"2022","journal-title":"ICLR"},{"key":"ref33","article-title":"Conditional image generation with pixelcnn decoders","volume":"29","author":"van den Oord","year":"2016","journal-title":"NeurIPS"}],"event":{"name":"2025 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2025,6,30]]},"location":"Rome, Italy","end":{"date-parts":[[2025,7,5]]}},"container-title":["2025 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11227166\/11227148\/11228652.pdf?arnumber=11228652","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T07:27:44Z","timestamp":1763191664000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11228652\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,30]]},"references-count":33,"URL":"https:\/\/doi.org\/10.1109\/ijcnn64981.2025.11228652","relation":{},"subject":[],"published":{"date-parts":[[2025,6,30]]}}}