{"type":"ScholarlyArticle","title":"SCAT: A Time Series Forecasting with Spectral Central Alternating Transformers","authors":[{"name":"Chengjie Zhou","slug":"chengjie-zhou","given":"Chengjie","family":"Zhou","affiliation":"School of Computer Science and Technology , Dalian University of Technology"},{"name":"Chao Che","slug":"chao-che","given":"Chao","family":"Che","affiliation":"Key Laboratory of Advanced Design and Intelligent Computing ( Dalian University) , Ministry of Education , Dalian University"},{"name":"Pengfei Wang","slug":"pengfei-wang","given":"Pengfei","family":"Wang","affiliation":"School of Computer Science and Technology , Dalian University of Technology"},{"name":"Qiang Zhang","slug":"qiang-zhang","given":"Qiang","family":"Zhang","affiliation":"School of Computer Science and Technology , Dalian University of Technology"}],"abstract":"Time series forecasting has essential applications across various domains. For instance, forecasting power time series can optimize energy usage and bolster grid stability and reliability. Existing models based on transformer architecture are limited to classical design, ignoring the impact of spatial information and noise on model architecture design. Therefore, we propose an atypical design of Transformer-based models for multivariate time series forecasting. This design consists of two critical components: (i) spectral clustering center of time series employed as the focal point for attention computation; (ii) alternating attention mechanism wherein each query transformer is compatible with spectral clustering centers, executing attention at the sequence level instead of the token level. The alternating design has a two-fold benefit: firstly, it eliminates the uncertainty noise present in the dependent variable sequence of the channel input, and secondly, it incorporates the Euclidean distance to mitigate the impact of extreme values on the attention matrix, thereby aligning predictions more closely to the sequence's natural progression. Experiments on ten real-world datasets, encompassing Wind, Electricity, Weather, and others, demonstrate that our Spectral Central Alternating Transformer (SCAT) outperforms state-of-the-art methods (SOTA) by an average of 42.8% in prediction performance in power time series forecasting.","doi":"10.24963/ijcai.2024/622","url":"https://ijcai.org/proceedings/2024/622","pdf":"https://ijcai.org/proceedings/2024/0622.pdf","markdown":"https://ijcai.org/proceedings/2024/622.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/622","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":622,"pages":{"first":5626,"last":5634},"keywords":["Machine Learning: ML: Time series and data streams"],"videos":[],"references":[{"position":1,"raw":"[Ariyo et al., 2014] Adebiyi A Ariyo, Adewumi O Adewumi, and Charles K Ayo. Stock price predic- tion using the arima model. In 2014 UKSim-AMSS 16th international conference on computer modelling and simulation, pages 106-112. IEEE, 2014.","title":"Stock price prediction using the arima model","authors":"Adewumi O Adebiyi A Ariyo, Charles K Adewumi, Ayo","year":"2014","venue":"UKSim-AMSS 16th international conference on computer modelling and simulation","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bai et al., 2018] Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. An empirical evaluation of generic convolu- tional and recurrent networks for sequence modeling. arXiv:1803.01271, 2018.","title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","authors":"J Zico Shaojie Bai, Vladlen Kolter, Koltun","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bianchi et al., 2020] Filippo Maria Bianchi, Daniele Grat- tarola, and Cesare Alippi. Spectral clustering with graph neural networks for graph pooling. In International con- ference on machine learning, pages 874-883. PMLR, 2020.","title":"Spectral clustering with graph neural networks for graph pooling","authors":"Maria Filippo, Daniele Bianchi, Cesare Grattarola, Alippi","year":"2020","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bonifati et al., 2022] Angela Bonifati, Francesco Del Buono, Francesco Guerra, and Donato Tiano. Time2feat: learning interpretable representations for multivariate time series clustering. Proceedings of the VLDB Endowment, 16(2):193-201, 2022.","title":"Time2feat: learning interpretable representations for multivariate time series clustering","authors":"Angela Bonifati, Francesco Del Buono, Francesco Guerra, Donato Tiano","year":"2022","venue":"Proceedings of the VLDB Endowment","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Box et al., 2015] George EP Box, Gwilym M Jenkins, Gre- gory C Reinsel, and Greta M Ljung. Time series analysis: forecasting and control. John Wiley & Sons, 2015.","title":"Time series analysis: forecasting and control","authors":"George Ep Box, M Gwilym, Gregory C Jenkins, Greta M Reinsel, Ljung","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Campos et al., 2023] David Campos, Miao Zhang, Bin Yang, Tung Kieu, Chenjuan Guo, and Christian S. Jensen. LightTS: Lightweight time series classification with adap- tive ensemble distillation. Proceedings of the ACM on Management of Data, 1(2):1-27, jun 2023.","title":"LightTS: Lightweight time series classification with adaptive ensemble distillation","authors":"David Campos, Miao Zhang, Bin Yang, Tung Kieu, Chenjuan Guo, Christian S Jensen","year":"2023","venue":"Proceedings of the ACM on Management of Data","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chan et al., 2015] William Chan, Navdeep Jaitly, Quoc V Le, and Oriol Vinyals. Listen, attend and spell. arXiv preprint arXiv:1508.01211, 2015.","title":"Listen, attend and spell","authors":"William Chan, Navdeep Jaitly, Oriol Quoc V Le, Vinyals","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chen et al., 2017] Dongdong Chen, Jiancheng Lv, and Yi Zhang. Unsupervised multi-manifold clustering by learning deep representation. In Workshops at the thirty- first AAAI conference on artificial intelligence, 2017.","title":"Unsupervised multi-manifold clustering by learning deep representation","authors":"Dongdong Chen, Jiancheng Lv, Yi Zhang","year":"2017","venue":"Workshops at the thirtyfirst AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Cui et al., 2021] Yue Cui, Jiandong Xie, and Kai Zheng. Historical inertia: A neglected but powerful baseline for long sequence time-series forecasting. In Proceedings of the 30th ACM international conference on information & knowledge management, pages 2965-2969, 2021.","title":"Historical inertia: A neglected but powerful baseline for long sequence time-series forecasting","authors":"Yue Cui, Jiandong Xie, Kai Zheng","year":"2021","venue":"Proceedings of the 30th ACM international conference on information & knowledge management","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Das et al., 2023] Abhimanyu Das, Weihao Kong, Andrew Leach, Rajat Sen, and Rose Yu. Long-term forecast- ing with tide: Time-series dense encoder. arXiv preprint arXiv:2304.08424, 2023.","title":"Long-term forecasting with tide: Time-series dense encoder","authors":"Abhimanyu Das, Weihao Kong, Andrew Leach, Rajat Sen, Rose Yu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Dosovitskiy et al., 2020] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Min- derer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.","title":"An image is worth 16x16 words: Transformers for image recognition at scale","authors":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Doubleday et al., 2020] Kate Doubleday, Stephen Jascourt, William Kleiber, and Bri-Mathias Hodge. Probabilistic solar power forecasting using bayesian model averaging. IEEE Transactions on Sustainable Energy, 12(1):325- 337, 2020.","title":"Probabilistic solar power forecasting using bayesian model averaging","authors":"Doubleday","year":"2020","venue":"IEEE Transactions on Sustainable Energy","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Elsaraiti and Merabet, 2022] Meftah Elsaraiti and Adel Merabet. Solar power forecasting using deep learning techniques. IEEE Access, 10:31692-31698, 2022.","title":"Meftah Elsaraiti and Adel Merabet. Solar power forecasting using deep learning techniques","authors":"Merabet Elsaraiti","year":"2022","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Fan et al., 2021] Lili Fan, Guifu Lu, Yong Wang, and Tao Liu. Block diagonal sparse subspace clustering. In 2021 13th International Conference on Wireless Communica- tions and Signal Processing (WCSP), pages 1-6, 2021.","title":"Block diagonal sparse subspace clustering","authors":"Lili Fan, Guifu Lu, Yong Wang, Tao Liu","year":"2021","venue":"13th International Conference on Wireless Communications and Signal Processing (WCSP)","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Graves and Graves, 2012] Alex Graves and Alex Graves. Long short-term memory. Supervised sequence labelling with recurrent neural networks, pages 37-45, 2012.","title":"Long short-term memory. Supervised sequence labelling with recurrent neural networks","authors":"Alex Graves, Alex Graves","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Khalifa et al., 2022] Ahmed A Khalifa, Abdul-Jalil Ibrahim, Abdulkarem I Amhamed, and Muftah H El- Naas. Accelerating the transition to a circular economy for net-zero emissions by 2050: a systematic review. Sustainability, 14(18):11656, 2022.","title":"Accelerating the transition to a circular economy for net-zero emissions by 2050: a systematic review","authors":"Abdul-Jalil Ahmed A Khalifa, Abdulkarem I Ibrahim, Muftah H Amhamed, El-Naas","year":"2022","venue":"Sustainability","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kitaev et al., 2020] Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. Reformer: The efficient transformer. In International Conference on Learning Representations, 2020.","title":"Reformer: The efficient transformer","authors":"Nikita Kitaev, Lukasz Kaiser, Anselm Levskaya","year":"2020","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Le Guen and Thome, 2020] Vincent Le Guen and Nicolas Thome. Probabilistic time series forecasting with shape and temporal diversity. Advances in Neural Information Processing Systems, 33:4427-4440, 2020.","title":"Vincent Le Guen and Nicolas Thome. Probabilistic time series forecasting with shape and temporal diversity","authors":"Le Guen, Thome","year":"2020","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lim and Zohren, 2021] Bryan Lim and Stefan Zohren. Time-series forecasting with deep learning: a sur- vey. Philosophical Transactions of the Royal Society A, 379(2194):20200209, 2021.","title":"Time-series forecasting with deep learning: a survey","authors":"Bryan Lim, Stefan Zohren","year":"2021","venue":"Philosophical Transactions of the Royal Society A","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Liu et al., 2022] Shizhan Liu, Hang Yu, Cong Liao, Jian- guo Li, Weiyao Lin, Alex X Liu, and Schahram Dust- dar. Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting. In Inter- national Conference on Learning Representations, 2022.","title":"Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting","authors":"Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X Liu, Schahram Dustdar","year":"2022","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Luo et al., 2018] Yonghong Luo, Xiangrui Cai, Ying Zhang, Jun Xu, et al. Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems, 31, 2018.","title":"Multivariate time series imputation with generative adversarial networks","authors":"Yonghong Luo, Xiangrui Cai, Ying Zhang, Jun Xu","year":"2018","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[MacQueen, 1967] James MacQueen. Some methods for classification and analysis of multivariate observations. Proceedings of the fifth Berkeley symposium on mathemat- ical statistics and probability, 1(14):281-297, 1967.","title":"Some methods for classification and analysis of multivariate observations","authors":"James Macqueen","year":"1967","venue":"Proceedings of the fifth Berkeley symposium on mathematical statistics and probability","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Moradzadeh et al., 2021] Arash Moradzadeh, Hamed Moayyed, Sahar Zakeri, Behnam Mohammadi-Ivatloo, and A Pedro Aguiar. Deep learning-assisted short-term load forecasting for sustainable management of energy in microgrid. Inventions, 6(1):15, 2021.","title":"Deep learning-assisted short-term load forecasting for sustainable management of energy in microgrid","authors":"Arash Moradzadeh, Hamed Moayyed, Sahar Zakeri, Pedro Behnam Mohammadi-Ivatloo, Aguiar","year":"2021","venue":"Inventions","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Nie et al., 2023] Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. A time series is worth 64 words: Long-term forecasting with transformers. In International Conference on Learning Representations, 2023.","title":"A time series is worth 64 words: Long-term forecasting with transformers","authors":"Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, Jayant Kalagnanam","year":"2023","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Panigrahi et al., 2018] Sibarama Panigrahi, Hi- mansu Sekhar Behera, and Ajith Abraham. A fuzzy filter based hybrid arima-ann model for time series forecasting. In Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016), pages 592-601. Springer, 2018.","title":"A fuzzy filter based hybrid arima-ann model for time series forecasting","authors":"Panigrahi","year":"2018","venue":"Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016)","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Peel and MacLahlan, 2000] DAVID Peel and G MacLahlan. Finite mixture models. John & Sons, 2000.","title":"Finite mixture models","authors":"David Peel, G Maclahlan","year":"2000","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Rushe and Mac Namee, 2019] Ellen Rushe and Brian Mac Namee. Anomaly detection in raw audio using deep autoregressive networks. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3597-3601. IEEE, 2019.","title":"Anomaly detection in raw audio using deep autoregressive networks","authors":"Mac Rushe, Ellen Namee, Brian Rushe, Namee Mac","year":"2019","venue":"ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Salinas et al., 2020] David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski. Deepar: Probabilistic forecasting with autoregressive recurrent networks. Inter- national Journal of Forecasting, 36(3):1181-1191, 2020.","title":"Deepar: Probabilistic forecasting with autoregressive recurrent networks","authors":"David Salinas, Valentin Flunkert, Jan Gasthaus, Tim Januschowski","year":"2020","venue":"International Journal of Forecasting","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Shao et al., 2022] Zezhi Shao, Zhao Zhang, Fei Wang, Wei Wei, and Yongjun Xu. Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecast- ing. In Proceedings of the 31st ACM International Con- ference on Information & Knowledge Management, CIKM '22, page 4454-4458, New York, NY, USA, 2022. Associ- ation for Computing Machinery.","title":"Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting","authors":"Zezhi Shao, Zhao Zhang, Fei Wang, Wei Wei, Yongjun Xu","year":"2022","venue":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM '22","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Shen et al., 2018] Zhipeng Shen, Yuanming Zhang, Jiawei Lu, Jun Xu, and Gang Xiao. Seriesnet: a generative time series forecasting model. In 2018 international joint con- ference on neural networks (IJCNN), pages 1-8. IEEE, 2018.","title":"Seriesnet: a generative time series forecasting model","authors":"Zhipeng Shen, Yuanming Zhang, Jiawei Lu, Jun Xu, Gang Xiao","year":"2018","venue":"2018 international joint conference on neural networks (IJCNN)","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Shih et al., 2019] Shun-Yao Shih, Fan-Keng Sun, and Hung-yi Lee. Temporal pattern attention for multivari- ate time series forecasting. Machine Learning, 108:1421- 1441, 2019.","title":"Temporal pattern attention for multivariate time series forecasting","authors":"Shun-Yao Shih, Fan-Keng Sun, Hung-Yi Lee","year":"2019","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Sys- tems, pages 5998-6008, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, Illia Polosukhin","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Voyant et al., 2017] Cyril Voyant, Gilles Notton, Soteris Kalogirou, Marie-Laure Nivet, Christophe Paoli, Fabrice Motte, and Alexis Fouilloy. Machine learning methods for solar radiation forecasting: A review. Renewable energy, 105:569-582, 2017.","title":"Machine learning methods for solar radiation forecasting: A review","authors":"Cyril Voyant, Gilles Notton, Soteris Kalogirou, Marie-Laure Nivet, Christophe Paoli, Fabrice Motte, Alexis Fouilloy","year":"2017","venue":"Renewable energy","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Wang and Deng, 2018] Mei Wang and Weihong Deng. Deep visual domain adaptation: A survey. Neurocomput- ing, 312:135-153, 2018.","title":"Deep visual domain adaptation: A survey","authors":"Mei Wang, Weihong Deng","year":"2018","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Wang et al., 2011] Xiaochen Wang, Peng Guo, and Xiaobin Huang. A review of wind power forecasting models. En- ergy procedia, 12:770-778, 2011.","title":"A review of wind power forecasting models","authors":"Xiaochen Wang, Peng Guo, Xiaobin Huang","year":"2011","venue":"Energy procedia","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wang et al., 2023] Huiqiang Wang, Jian Peng, Feihu Huang, Jince Wang, Junhui Chen, and Yifei Xiao. Micn: Multi-scale local and global context modeling for long- term series forecasting. In International Conference on Learning Representations, 2023.","title":"Micn: Multi-scale local and global context modeling for longterm series forecasting","authors":"Huiqiang Wang, Jian Peng, Feihu Huang, Jince Wang, Junhui Chen, Yifei Xiao","year":"2023","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Woo et al., 2022] Gerald Woo, Chenghao Liu, Doyen Sa- hoo, Akshat Kumar, and Steven C. H. Hoi. Etsformer: Exponential smoothing transformers for time-series fore- casting. In International Conference on Machine Learn- ing, 2022.","title":"Etsformer: Exponential smoothing transformers for time-series forecasting","authors":"Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven C H Hoi","year":"2022","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wu et al., 2021] Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. Autoformer: Decomposition transform- ers with auto-correlation for long-term series forecast- ing. Advances in Neural Information Processing Systems, 34:22419-22430, 2021.","title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","authors":"Wu","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wu et al., 2023] Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. Timesnet: Temporal 2d-variation modeling for general time series analysis. In International Conference on Learning Rep- resentations, 2023.","title":"Timesnet: Temporal 2d-variation modeling for general time series analysis","authors":"Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, Mingsheng Long","year":"2023","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Xiao et al., 2023] Fei Xiao, Yuncheng Wu, Meihui Zhang, Gang Chen, and Beng Chin Ooi. Mint: Detecting fraud- ulent behaviors from time-series relational data. Proceed- ings of the VLDB Endowment, 16(12):3610-3623, 2023.","title":"Mint: Detecting fraudulent behaviors from time-series relational data","authors":"Fei Xiao, Yuncheng Wu, Meihui Zhang, Gang Chen, Beng Chin Ooi","year":"2023","venue":"Proceedings of the VLDB Endowment","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Yao et al., 2023] Yuanyuan Yao, Dimeng Li, Hailiang Jie, Hailiang Jie, Tianyi Li, Jie Chen, Jiaqi Wang, Feifei Li, and Yunjun Gao. Simplets: An efficient and universal model selection framework for time series forecasting. Proceedings of the VLDB Endowment, 16(12):3741-3753, 2023.","title":"Simplets: An efficient and universal model selection framework for time series forecasting","authors":"Yuanyuan Yao, Dimeng Li, Hailiang Jie, Hailiang Jie, Tianyi Li, Jie Chen, Jiaqi Wang, Feifei Li, Yunjun Gao","year":"2023","venue":"Proceedings of the VLDB Endowment","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Yoon et al., 2019] Jinsung Yoon, Daniel Jarrett, and Mi- haela Van der Schaar. Time-series generative adversarial networks. Advances in neural information processing sys- tems, 32, 2019.","title":"Time-series generative adversarial networks","authors":"Yoon","year":"2019","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zeng et al., 2023] Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. Are transformers effective for time series fore- casting? Proceedings of the AAAI Conference on Artificial Intelligence, 37(9):11121-11128, Jun. 2023.","title":"Are transformers effective for time series forecasting","authors":"Ailing Zeng, Muxi Chen, Lei Zhang, Qiang Xu","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhang et al., 2018] Zheng Zhang, Yong Xu, Ling Shao, and Jian Yang. Discriminative block-diagonal represen- tation learning for image recognition. IEEE Transactions on Neural Networks and Learning Systems, 29(7):3111- 3125, 2018.","title":"Discriminative block-diagonal representation learning for image recognition","authors":"Zheng Zhang, Yong Xu, Ling Shao, Jian Yang","year":"2018","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhang et al., 2021] Dejiao Zhang, Feng Nan, Xiaokai Wei, Shangwen Li, Henghui Zhu, Kathleen McKeown, Ramesh Nallapati, Andrew Arnold, and Bing Xiang. Supporting clustering with contrastive learning, 2021.","title":"Supporting clustering with contrastive learning","authors":"Zhang","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zhou et al., 2021] Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 11106- 11115, 2021.","title":"Informer: Beyond efficient transformer for long sequence time-series forecasting","authors":"Zhou","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Zhou et al., 2022] Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. Fedformer: Fre- quency enhanced decomposed transformer for long-term series forecasting. In International Conference on Ma- chine Learning, pages 27268-27286. PMLR, 2022.","title":"Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting","authors":"Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin","year":"2022","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}