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Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF).
Language models are few-shot learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 1901
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Supervised multimodal bitransformers for classifying images and text
Kiela, D.; Bhooshan, S.; Firooz, H.; Perez, E.; and Testuggine, D. 2019 · 1909
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Forecasting natural gas consumption in Istanbul using neural networks and multivariate time series methods
Demirel, Ö. F.; Zaim, S.; Çalişkan, A.; and Özuyar, P. 2012 · 2012
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Efficient estimation of word representations in vector space
Mikolov, T.; Chen, K.; Corrado, G.; and Dean, J. 2013 · 2013
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Copula methods for forecasting multivariate time series
Patton, A. 2013 · 2013
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Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
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An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Bai, S.; Kolter, J. Z.; and Koltun, V. 2018 · 2018
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The M4 Competition: Results, findings, conclusion and way forward
Makridakis, S.; Spiliotis, E.; and Assimakopoulos, V. 2018 · 2018
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N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
Oreshkin, B. N.; Carpov, D.; Chapados, N.; and Bengio, Y. 2019 · 2019
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Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; Sutskever, I.; et al. 2019 · 2019
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Multivariate time series dataset for space weather data analytics
Angryk, R. A.; Martens, P. C.; Aydin, B.; Kempton, D.; Mahajan, S. S.; Basodi, S.; Ahmadzadeh, A.; Cai, X.; Filali Boubrahimi, S.; Hamdi, S. M.; et al. 2020 · 2020
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A simple framework for contrastive learning of visual representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. 2020 · 2020
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LoRA: Low-rank adaptation of large language models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
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Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
Wu, H.; Xu, J.; Wang, J.; and Long, M. 2021 · 2021
Cited alongside, same era.
Modeling protein using large-scale pretrain language model
Xiao, Y.; Qiu, J.; Li, Z.; Hsieh, C.-Y.; and Tang, J. 2021 · 2021
Cited alongside, same era.
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
Challu, C.; Olivares, K. G.; Oreshkin, B. N.; Garza, F.; Mergenthaler, M.; and Dubrawski, A. 2022 · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Kojima, T.; Gu, S. S.; Reid, M.; Matsuo, Y.; and Iwasawa, Y. 2022 · 2022
Cited alongside, same era.
MICN: Multi-scale local and global context modeling for long-term series forecasting
Wang, H.; Peng, J.; Huang, F.; Wang, J.; Chen, J.; and Xiao, Y. 2022 · 2022
Cited alongside, same era.
Reprogramming pretrained language models for protein sequence representation learning
Vinod, R.; Chen, P.-Y.; and Das, P. 2023 · 2023
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TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
Wu, H.; Hu, T.; Liu, Y.; Zhou, H.; Wang, J.; and Long, M. 2023 · 2023
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Are transformers effective for time series forecasting?
Zeng, A.; Chen, M.; Zhang, L.; and Xu, Q. 2023 · 2023
Later among the works it cites.
Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting
Zhang, Y.; and Yan, J. 2023 · 2023
Later among the works it cites.
One Fits All: Power General Time Series Analysis by Pretrained LM
Zhou, T.; Niu, P.; Wang, X.; Sun, L.; and Jin, R. 2023 · 2023
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Transformers in time series: A survey
Wen, Q.; Zhou, T.; Zhang, C.; Chen, W.; Ma, Z.; Yan, J.; and Sun, L. 2022 · 2022
Cited alongside, same era.
ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
Woo, G.; Liu, C.; Sahoo, D.; Kumar, A.; and Hoi, S. C. H. 2022 · 2022
Cited alongside, same era.
FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting
Zhou, T.; Ma, Z.; Wen, Q.; Wang, X.; Sun, L.; and Jin, R. 2022 · 2022
Cited alongside, same era.
LLM4TS: Two-stage fine-tuning for time-series forecasting with pre-trained llms
Chang, C.; Peng, W.-C.; and Chen, T.-F. 2023 · 2023
Cited alongside, same era.
Long-term Forecasting with TiDE: Time-series Dense Encoder
Das, A.; Kong, W.; Leach, A.; Sen, R.; and Yu, R. 2023 · 2023
Cited alongside, same era.
Cross-modal distillation for speaker recognition
Jin, Y.; Hu, G.; Chen, H.; Miao, D.; Hu, L.; and Zhao, C. 2023 · 2023
Cited alongside, same era.
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
Nie, Y.; H. Nguyen, N.; Sinthong, P.; and Kalagnanam, J. 2023 · 2023
Cited alongside, same era.
TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
Cao, D.; Jia, F.; Arik, S. O.; Pfister, T.; Zheng, Y.; Ye, W.; and Liu, Y. 2024 · 2024
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Periodicity Decoupling Framework for Long-term Series Forecasting
Dai, T.; Wu, B.; Liu, P.; Li, N.; Bao, J.; Jiang, Y.; and Xia, S.-T. 2024 · 2024
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Textually pretrained speech language models
Hassid, M.; Remez, T.; Nguyen, T. A.; Gat, I.; Conneau, A.; Kreuk, F.; Copet, J.; Defossez, A.; Synnaeve, G.; Dupoux, E.; et al. 2024 · 2024
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Time-LLM: Time series forecasting by reprogramming large language models
Jin, M.; Wang, S.; Ma, L.; Chu, Z.; Zhang, J. Y.; Shi, X.; Chen, P.-Y.; Liang, Y.; Li, Y.-F.; Pan, S.; et al. 2024 · 2024
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FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting
Li, Z.; Qiu, X.; Chen, P.; Wang, Y.; Cheng, H.; Shu, Y.; Hu, J.; Guo, C.; Zhou, A.; Wen, Q.; et al. 2024 · 2024
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iTransformer: Inverted transformers are effective for time series forecasting
Liu, Y.; Hu, T.; Zhang, H.; Wu, H.; Wang, S.; Ma, L.; and Long, M. 2024 · 2024
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TEST: Text Prototype Aligned Embedding to Activate LLM’s Ability for Time Series
Sun, C.; Li, H.; Li, Y.; and Hong, S. 2024 · 2024
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Verma, G.; Choi, M.; Sharma, K.; Watson-Daniels, J.; Oh, S.; and Kumar, S. 2024 · 2024
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