{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T21:16:05Z","timestamp":1767906965335,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T00:00:00Z","timestamp":1752969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"TJU-Wenge joint laboratory funding"},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276188"],"award-info":[{"award-number":["62276188"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,20]]},"DOI":"10.1145\/3690624.3709228","type":"proceedings-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T18:48:32Z","timestamp":1743792512000},"page":"1173-1184","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Quantum Time-index Models with Reservoir for Time Series Forecasting"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9764-5679","authenticated-orcid":false,"given":"Wenbo","family":"Qiao","sequence":"first","affiliation":[{"name":"School of New Media and Communication, Tianjin University, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9613-4774","authenticated-orcid":false,"given":"Jiaming","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of New Media and Communication, Tianjin University, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0228-9330","authenticated-orcid":false,"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"}]}],"member":"320","published-online":{"date-parts":[[2025,7,20]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"SIR, and SIS epidemic models. Mathematical biosciences","author":"Allen Linda JS","year":"1994","unstructured":"Linda JS Allen. 1994. Some discrete-time SI, SIR, and SIS epidemic models. Mathematical biosciences, Vol. 124, 1 (1994), 83--105."},{"key":"e_1_3_2_2_2_1","volume-title":"International conference on learning representations.","author":"Antoniou Antreas","year":"2018","unstructured":"Antreas Antoniou, Harrison Edwards, and Amos Storkey. 2018. How to train your MAML. In International conference on learning representations."},{"key":"e_1_3_2_2_3_1","volume-title":"Recurrent Quantum Neural Networks. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020","author":"Bausch Johannes","year":"2020","unstructured":"Johannes Bausch. 2020. Recurrent Quantum Neural Networks. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual, Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/0ec96be397dd6d3cf2fecb4a2d627c1c-Abstract.html"},{"key":"e_1_3_2_2_4_1","volume-title":"Cost function dependent barren plateaus in shallow parametrized quantum circuits. Nature communications","author":"Cerezo Marco","year":"2021","unstructured":"Marco Cerezo, Akira Sone, Tyler Volkoff, Lukasz Cincio, and Patrick J Coles. 2021. Cost function dependent barren plateaus in shallow parametrized quantum circuits. Nature communications, Vol. 12, 1 (2021), 1791."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/AAAI.V37I6.25854"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9747369"},{"key":"e_1_3_2_2_7_1","unstructured":"Daryl J Daley and David Vere-Jones. 2007. An introduction to the theory of point processes: volume II: general theory and structure. Springer Science & Business Media."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/219717.219797"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1142\/9789812833709_0030"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-45015-4"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevApplied.8.024030"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24797-2"},{"key":"e_1_3_2_2_13_1","volume-title":"Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752","author":"Gu Albert","year":"2023","unstructured":"Albert Gu and Tri Dao. 2023. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023)."},{"key":"e_1_3_2_2_14_1","volume-title":"Long short-term memory. Neural computation","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_2_15_1","volume-title":"Adaptive nonlinear system identification with echo state networks. Advances in neural information processing systems","author":"Jaeger Herbert","year":"2002","unstructured":"Herbert Jaeger. 2002. Adaptive nonlinear system identification with echo state networks. Advances in neural information processing systems, Vol. 15 (2002)."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2020.07.038"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210006"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539236"},{"key":"e_1_3_2_2_19_1","volume-title":"Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems","author":"Li Shiyang","year":"2019","unstructured":"Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan. 2019. Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8--14, 2019, Vancouver, BC, Canada, Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alch\u00e9-Buc, Emily B. Fox, and Roman Garnett (Eds.). 5244--5254. https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/6775a0635c302542da2c32aa19d86be0-Abstract.html"},{"key":"e_1_3_2_2_20_1","volume-title":"Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems","author":"Liu Yong","year":"2022","unstructured":"Yong Liu, Haixu Wu, Jianmin Wang, and Mingsheng Long. 2022. Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022, Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, and A. Oh (Eds.). http:\/\/papers.nips.cc\/paper_files\/paper\/2022\/hash\/4054556fcaa934b0bf76da52cf4f92cb-Abstract-Conference.html"},{"key":"e_1_3_2_2_21_1","volume-title":"The neural hawkes process: A neurally self-modulating multivariate point process. Advances in neural information processing systems","author":"Mei Hongyuan","year":"2017","unstructured":"Hongyuan Mei and Jason M Eisner. 2017. The neural hawkes process: A neurally self-modulating multivariate point process. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.98.032309"},{"key":"e_1_3_2_2_23_1","volume-title":"Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach. Physical review letters","author":"Pathak Jaideep","year":"2018","unstructured":"Jaideep Pathak, Brian Hunt, Michelle Girvan, Zhixin Lu, and Edward Ott. 2018. Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach. Physical review letters, Vol. 120, 2 (2018), 024102."},{"key":"e_1_3_2_2_24_1","volume-title":"Quantum Topic Model: Topic Modeling Using Variational Quantum Circuits. In ICASSP 2024--2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 5895--5899","author":"Qiao Wenbo","year":"2024","unstructured":"Wenbo Qiao, Peng Zhang, Jiaming Zhao, and Chang Yang. 2024. Quantum Topic Model: Topic Modeling Using Variational Quantum Circuits. In ICASSP 2024--2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 5895--5899."},{"key":"e_1_3_2_2_25_1","volume-title":"Tfb: Towards comprehensive and fair benchmarking of time series forecasting methods. arXiv preprint arXiv:2403.20150","author":"Qiu Xiangfei","year":"2024","unstructured":"Xiangfei Qiu, Jilin Hu, Lekui Zhou, Xingjian Wu, Junyang Du, Buang Zhang, Chenjuan Guo, Aoying Zhou, Christian S Jensen, Zhenli Sheng, et al. 2024. Tfb: Towards comprehensive and fair benchmarking of time series forecasting methods. arXiv preprint arXiv:2403.20150 (2024)."},{"key":"e_1_3_2_2_26_1","volume-title":"International Conference on Machine Learning. PMLR, 5301--5310","author":"Rahaman Nasim","year":"2019","unstructured":"Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville. 2019. On the spectral bias of neural networks. In International Conference on Machine Learning. PMLR, 5301--5310."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978--3--540--28650--9_4"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"David Salinas Valentin Flunkert Jan Gasthaus and Tim Januschowski. 2020. DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International journal of forecasting Vol. 36 3 (2020) 1181--1191.","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.103.032430"},{"key":"e_1_3_2_2_30_1","volume-title":"Implicit neural representations with periodic activation functions. Advances in neural information processing systems","author":"Sitzmann Vincent","year":"2020","unstructured":"Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. 2020. Implicit neural representations with periodic activation functions. Advances in neural information processing systems, Vol. 33 (2020), 7462--7473."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"crossref","unstructured":"Peter Steiner Azarakhsh Jalalvand Simon Stone and Peter Birkholz. 2021. PyRCN: A Toolbox for Exploration and Application of Reservoir Computing Networks. arxiv: 2103.04807 [cs.LG]","DOI":"10.1016\/j.engappai.2022.104964"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.103.052414"},{"key":"e_1_3_2_2_33_1","volume-title":"Fourier features let networks learn high frequency functions in low dimensional domains. Advances in neural information processing systems","author":"Tancik Matthew","year":"2020","unstructured":"Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng. 2020. Fourier features let networks learn high frequency functions in low dimensional domains. Advances in neural information processing systems, Vol. 33 (2020), 7537--7547."},{"key":"e_1_3_2_2_34_1","volume-title":"GraphQNTK: Quantum Neural Tangent Kernel for Graph Data. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022","author":"Tang Yehui","year":"2022","unstructured":"Yehui Tang and Junchi Yan. 2022. GraphQNTK: Quantum Neural Tangent Kernel for Graph Data. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022, Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, and A. Oh (Eds.). http:\/\/papers.nips.cc\/paper_files\/paper\/2022\/hash\/285b06e0dd856f20591b0a5beb954151-Abstract-Conference.html"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1080\/00031305.2017.1380080"},{"key":"e_1_3_2_2_36_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330704"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA53966.2022.00057"},{"key":"e_1_3_2_2_39_1","volume-title":"Timemixer: Decomposable multiscale mixing for time series forecasting. arXiv preprint arXiv:2405.14616","author":"Wang Shiyu","year":"2024","unstructured":"Shiyu Wang, Haixu Wu, Xiaoming Shi, Tengge Hu, Huakun Luo, Lintao Ma, James Y Zhang, and Jun Zhou. 2024. Timemixer: Decomposable multiscale mixing for time series forecasting. arXiv preprint arXiv:2405.14616 (2024)."},{"key":"e_1_3_2_2_40_1","volume-title":"Hoi","author":"Woo Gerald","year":"2022","unstructured":"Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, and Steven C. H. Hoi. 2022. ETSformer: Exponential Smoothing Transformers for Time-series Forecasting. CoRR, Vol. abs\/2202.01381 (2022). showeprint[arXiv]2202.01381 https:\/\/arxiv.org\/abs\/2202.01381"},{"key":"e_1_3_2_2_41_1","volume-title":"Learning Deep Time-index Models for Time Series Forecasting. In International Conference on Machine Learning, ICML 2023","volume":"37237","author":"Woo Gerald","year":"2023","unstructured":"Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, and Steven C. H. Hoi. 2023. Learning Deep Time-index Models for Time Series Forecasting. In International Conference on Machine Learning, ICML 2023, 23--29 July 2023, Honolulu, Hawaii, USA (Proceedings of Machine Learning Research, Vol. 202), Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (Eds.). PMLR, 37217--37237. https:\/\/proceedings.mlr.press\/v202\/woo23b.html"},{"key":"e_1_3_2_2_42_1","unstructured":"Haixu Wu Jiehui Xu Jianmin Wang and Mingsheng Long. 2021. Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021 NeurIPS 2021 December 6--14 2021 virtual Marc'Aurelio Ranzato Alina Beygelzimer Yann N. Dauphin Percy Liang and Jennifer Wortman Vaughan (Eds.). 22419--22430. https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/bcc0d400288793e8bdcd7c19a8ac0c2b-Abstract.html"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scib.2023.08.040"},{"key":"e_1_3_2_2_45_1","volume-title":"Frequency principle: Fourier analysis sheds light on deep neural networks. arXiv preprint arXiv:1901.06523","author":"John Xu Zhi-Qin","year":"2019","unstructured":"Zhi-Qin John Xu, Yaoyu Zhang, Tao Luo, Yanyang Xiao, and Zheng Ma. 2019. Frequency principle: Fourier analysis sheds light on deep neural networks. arXiv preprint arXiv:1901.06523 (2019)."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539327"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.347"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26276"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599542"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467297"},{"key":"e_1_3_2_2_51_1","first-page":"27810","article-title":"Power and limitations of single-qubit native quantum neural networks","volume":"35","author":"Yu Zhan","year":"2022","unstructured":"Zhan Yu, Hongshun Yao, Mujin Li, and Xin Wang. 2022. Power and limitations of single-qubit native quantum neural networks. Advances in Neural Information Processing Systems, Vol. 35 (2022), 27810--27823.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_52_1","volume-title":"Quantum Implicit Neural Representations. In Forty-first International Conference on Machine Learning. https:\/\/openreview.net\/forum?id=50vc4HBuKU","author":"Zhao Jiaming","year":"2024","unstructured":"Jiaming Zhao, Wenbo Qiao, Peng Zhang, and Hui Gao. 2024. Quantum Implicit Neural Representations. In Forty-first International Conference on Machine Learning. https:\/\/openreview.net\/forum?id=50vc4HBuKU"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"e_1_3_2_2_54_1","volume-title":"FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting. In International Conference on Machine Learning, ICML 2022","volume":"27286","author":"Zhou Tian","year":"2022","unstructured":"Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting. In International Conference on Machine Learning, ICML 2022, 17--23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research, Vol. 162), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesv\u00e1ri, Gang Niu, and Sivan Sabato (Eds.). PMLR, 27268--27286. https:\/\/proceedings.mlr.press\/v162\/zhou22g.html"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709228","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3690624.3709228","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T15:44:52Z","timestamp":1755359092000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709228"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,20]]},"references-count":54,"alternative-id":["10.1145\/3690624.3709228","10.1145\/3690624"],"URL":"https:\/\/doi.org\/10.1145\/3690624.3709228","relation":{},"subject":[],"published":{"date-parts":[[2025,7,20]]},"assertion":[{"value":"2025-07-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}