{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T06:12:44Z","timestamp":1774419164862,"version":"3.50.1"},"reference-count":21,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T00:00:00Z","timestamp":1743897600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T00:00:00Z","timestamp":1743897600000},"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":[[2025,4,6]]},"DOI":"10.1109\/icassp49660.2025.10888704","type":"proceedings-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T17:15:02Z","timestamp":1741799702000},"page":"1-5","source":"Crossref","is-referenced-by-count":0,"title":["InjectTST: Injecting Global Information into Independent Channels for Long Time Series Forecasting"],"prefix":"10.1109","author":[{"given":"Ce","family":"Chi","sequence":"first","affiliation":[{"name":"JIUTIAN Team, China Mobile Research Institute,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Wang","sequence":"additional","affiliation":[{"name":"JIUTIAN Team, China Mobile Research Institute,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kexin","family":"Yang","sequence":"additional","affiliation":[{"name":"JIUTIAN Team, China Mobile Research Institute,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyan","family":"Song","sequence":"additional","affiliation":[{"name":"JIUTIAN Team, China Mobile Research Institute,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Di","family":"Jin","sequence":"additional","affiliation":[{"name":"JIUTIAN Team, China Mobile Research Institute,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Zhu","sequence":"additional","affiliation":[{"name":"JIUTIAN Team, China Mobile Research Institute,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Deng","sequence":"additional","affiliation":[{"name":"JIUTIAN Team, China Mobile Research Institute,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junlan","family":"Feng","sequence":"additional","affiliation":[{"name":"JIUTIAN Team, China Mobile Research Institute,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/b978-0-444-62731-5.00016-6"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-020-0548-x"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.12644"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2021.3098557"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/759"},{"key":"ref6","article-title":"Tempo: Prompt-based generative pre-trained transformer for time series forecasting","volume-title":"The Twelfth International Conference on Learning Representations, ICLR","author":"Cao"},{"key":"ref7","article-title":"LLM4TS: two-stage fine-tuning for time-series forecasting with pre-trained llms","author":"Chang","year":"2023"},{"key":"ref8","article-title":"Multi-resolution time-series transformer for long-term forecasting","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Zhang"},{"key":"ref9","first-page":"22 419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref10","article-title":"iTrans-former: Inverted transformers are effective for time series forecasting","volume-title":"The Twelfth International Conference on Learning Representations, ICLR","author":"Liu"},{"key":"ref11","article-title":"Timesnet: Temporal 2d-variation modeling for general time series analysis","volume-title":"The Eleventh International Conference on Learning Representations, ICLR","author":"Wu"},{"key":"ref12","article-title":"Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting","volume-title":"The Eleventh International Conference on Learning Representations","author":"Zhang"},{"key":"ref13","article-title":"Frequency-domain mlps are more effective learners in time series forecasting","volume-title":"Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS","author":"Yi"},{"key":"ref14","first-page":"46 885","article-title":"Crossgnn: Confronting noisy multivariate time series via cross interaction refinement","volume":"36","author":"Huang","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref15","article-title":"A time series is worth 64 words: Long-term forecasting with transformers","volume-title":"The Eleventh International Conference on Learning Representations","author":"Nie"},{"key":"ref16","article-title":"One fits all: Power general time series analysis by pretrained LM","volume-title":"Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS","author":"Zhou"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2024.3400008"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref20","first-page":"27 268","article-title":"Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting","volume-title":"International Conference on Machine Learning","author":"Zhou"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"}],"event":{"name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","location":"Hyderabad, India","start":{"date-parts":[[2025,4,6]]},"end":{"date-parts":[[2025,4,11]]}},"container-title":["ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10887540\/10887541\/10888704.pdf?arnumber=10888704","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T05:20:58Z","timestamp":1774416058000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10888704\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,6]]},"references-count":21,"URL":"https:\/\/doi.org\/10.1109\/icassp49660.2025.10888704","relation":{},"subject":[],"published":{"date-parts":[[2025,4,6]]}}}