Fetching the paper…
Reading the bibliography…
The balance between model capacity and generalization has been a key focus of recent discussions in long-term time series forecasting.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
Earlier work this paper cites.
Reversible instance normalization for accurate time-series forecasting against distribution shift. In International Conference on Learning Representations
Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, and Jaegul Choo. 2021 · 2021
Earlier work this paper cites.
Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021 · 2021
Earlier work this paper cites.
Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence , Vol. 35. 11106–11115
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021 · 2021
Earlier work this paper cites.
A time series is worth 64 words: Long-term forecasting with transformers
Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. 2022 · 2022
Earlier work this paper cites.
Timesnet: Temporal 2d-variation modeling for general time series analysis. In The eleventh international conference on learning representations
Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. 2022 · 2022
Earlier work this paper cites.
Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In The eleventh international conference on learning representations
Yunhao Zhang and Junchi Yan. 2022 · 2022
Earlier work this paper cites.
Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International conference on machine learning . PMLR, 27268–27286
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022 · 2022
Earlier work this paper cites.
Tsmixer: An all-mlp architecture for time series forecasting
Si-An Chen, Chun-Liang Li, Nate Yoder, Sercan O Arik, and Tomas Pfister. 2023 · 2023
Cited alongside, same era.
Revisiting long-term time series forecasting: An investigation on linear mapping
Zhe Li, Shiyi Qi, Yiduo Li, and Zenglin Xu. 2023 · 2023
Cited alongside, same era.
itransformer: Inverted transformers are effective for time series forecasting
Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, and Mingsheng Long. 2023 · 2023
Cited alongside, same era.
ImputeFormer: Low rankness-induced transformers for generalizable spatiotemporal imputation
Tong Nie, Guoyang Qin, Wei Ma, Yuewen Mei, and Jian Sun. 2023a · 2023
Cited alongside, same era.
Contextualizing MLP-Mixers Spatiotemporally for Urban Data Forecast at Scale
GAFormer: Enhancing Timeseries Transformers Through Group-Aware Embeddings. In The Twelfth International Conference on Learning Representations
Jingyun Xiao, Ran Liu, and Eva L Dyer. 2023 · 2023
Later among the works it cites.
Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence , Vol. 37. 11121–11128
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023 · 2023
Later among the works it cites.
From Similarity to Superiority: Channel Clustering for Time Series Forecasting
Jialin Chen, Jan Eric Lenssen, Aosong Feng, Weihua Hu, Matthias Fey, Leandros Tassiulas, Jure Leskovec, and Rex Ying. 2024 · 2024
Closest in time.
Taming local effects in graph-based spatiotemporal forecasting
Andrea Cini, Ivan Marisca, Daniele Zambon, and Cesare Alippi. 2024 · 2024
Closest in time.
The capacity and robustness trade-off: Revisiting the channel independent strategy for multivariate time series forecasting
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tong Nie, Guoyang Qin, Lijun Sun, Wei Ma, Yu Mei, and Jian Sun. 2023b · 2023
Cited alongside, same era.
Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach
Tong Nie, Guoyang Qin, Yunpeng Wang, and Jian Sun. 2023c · 2023
Cited alongside, same era.
Stock ranking prediction using a graph aggregation network based on stock price and stock relationship information
Guowei Song, Tianlong Zhao, Suwei Wang, Hua Wang, and Xuemei Li. 2023 · 2023
Cited alongside, same era.
TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting. In The Twelfth International Conference on Learning Representations
Shiyu Wang, Haixu Wu, Xiaoming Shi, Tengge Hu, Huakun Luo, Lintao Ma, James Y Zhang, and JUN ZHOU. 2023 · 2023
Cited alongside, same era.
Lu Han, Han-Jia Ye, and De-Chuan Zhan. 2024 · 2024
Closest in time.
Largest: A benchmark dataset for large-scale traffic forecasting
Xu Liu, Yutong Xia, Yuxuan Liang, Junfeng Hu, Yiwei Wang, Lei Bai, Chao Huang, Zhenguang Liu, Bryan Hooi, and Roger Zimmermann. 2024 · 2024
Closest in time.
Frequency-domain MLPs are more effective learners in time series forecasting
Kun Yi, Qi Zhang, Wei Fan, Shoujin Wang, Pengyang Wang, Hui He, Ning An, Defu Lian, Longbing Cao, and Zhendong Niu. 2024 · 2024
Closest in time.
Lifan Zhao and Yanyan Shen. 2024 · 2024
Closest in time.