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Large language models (LLMs) are being applied to time series forecasting.
Empirical evaluation of gated recurrent neural networks on sequence modeling
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio · 2014
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ElectricityLoadDiagrams20112014
A. Trindade · 2015
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Probabilistic demand forecasting at scale
J.-H. Böse, V. Flunkert, J. Gasthaus, T. Januschowski, D. Lange, D. Salinas, S. Schelter, M. Seeger, and Y. Wang · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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Modeling long-and short-term temporal patterns with deep neural networks
G. Lai, W.-C. Chang, Y. Yang, and H. Liu · 2018
Earlier work this paper cites.
Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al · 2019
Earlier work this paper cites.
Financial time series forecasting with deep learning: A systematic literature review: 2005–2019
O. B. Sezer, M. U. Gudelek, and A. M. Ozbayoglu · 2020
Earlier work this paper cites.
Monash time series forecasting archive
R. Godahewa, C. Bergmeir, G. I. Webb, R. J. Hyndman, and P. Montero-Manso · 2021
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Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
H. Wu, J. Xu, J. Wang, and M. Long · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting
H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang · 2021
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Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
T. Zhou, Z. Ma, Q. Wen, X. Wang, L. Sun, and R. Jin · 2022
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Audiolm: a language modeling approach to audio generation
Z. Borsos, R. Marinier, D. Vincent, E. Kharitonov, O. Pietquin, M. Sharifi, D. Roblek, O. Teboul, D. Grangier, M. Tagliasacchi, et al · 2023
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Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms
C. Chang, W.-C. Peng, and T.-F. Chen · 2023
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Imagebind: One embedding space to bind them all
R. Girdhar, A. El-Nouby, Z. Liu, M. Singh, K. V. Alwala, A. Joulin, and I. Misra · 2023
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Large language models are zero-shot time series forecasters
N. Gruver, M. Finzi, S. Qiu, and A. G. Wilson · 2023
Cited alongside, same era.
Revisiting long-term time series forecasting: An investigation on linear mapping
Z. Li, S. Qi, Y. Li, and Z. Xu · 2023
Cited alongside, same era.
Large language models are few-shot health learners
X. Liu, D. McDuff, G. Kovacs, I. Galatzer-Levy, J. Sunshine, J. Zhan, M.-Z. Poh, S. Liao, P. Di Achille, and S. Patel · 2023
Cited alongside, same era.
Time series prediction using deep learning methods in healthcare
M. A. Morid, O. R. L. Sheng, and J. Dunbar · 2023
Cited alongside, same era.
A time series is worth 64 words: Long-term forecasting with transformers
Y. Nie, N. H. Nguyen, P. Sinthong, and J. Kalagnanam · 2023
Cited alongside, same era.
Tempo: Prompt-based generative pre-trained transformer for time series forecasting
D. Cao, F. Jia, S. O. Arik, T. Pfister, Y. Zheng, W. Ye, and Y. Liu · 2024
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Sociodojo: Building lifelong analytical agents with real-world text and time series
J. Cheng and P. Chin · 2024
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Towards time series reasoning with llms
W. Chow, L. Gardiner, H. T. Hallgrímsson, M. A. Xu, and S. Y. Ren · 2024
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A decoder-only foundation model for time-series forecasting
A. Das, W. Kong, R. Sen, and Y. Zhou · 2024
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Supporting physical activity behavior change with llm-based conversational agents
M. Jörke, S. Sapkota, L. Warkenthien, N. Vainio, P. Schmiedmayer, E. Brunskill, and J. Landay · 2024
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Lag-llama: Towards foundation models for time series forecasting
K. Rasul, A. Ashok, A. R. Williams, A. Khorasani, G. Adamopoulos, R. Bhagwatkar, M. Biloš, H. Ghonia, N. Hassen, A. Schneider, et al · 2023
Cited alongside, same era.
Time series modeling at scale: A universal representation across tasks and domains
S. J. Talukder and G. Gkioxari · 2023
Cited alongside, same era.
Llama: Open and efficient foundation language models (2023)
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al · 2023
Cited alongside, same era.
Leveraging vision-language models for granular market change prediction
C. Wimmer and N. Rekabsaz · 2023
Cited alongside, same era.
Timesnet: Temporal 2d-variation modeling for general time series analysis
H. Wu, T. Hu, Y. Liu, H. Zhou, J. Wang, and M. Long · 2023
Cited alongside, same era.
Promptcast: A new prompt-based learning paradigm for time series forecasting
H. Xue and F. D. Salim · 2023
Cited alongside, same era.
Are transformers effective for time series forecasting?
A. Zeng, M. Chen, L. Zhang, and Q. Xu · 2023
Cited alongside, same era.
Learning to embed time series patches independently
S. Lee, T. Park, and K. Lee · 2024
Closest in time.
A reflective llm-based agent to guide zero-shot cryptocurrency trading
Y. Li, B. Luo, Q. Wang, N. Chen, X. Liu, and B. He · 2024
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Language models still struggle to zero-shot reason about time series
M. A. Merrill, M. Tan, V. Gupta, T. Hartvigsen, and T. Althoff · 2024
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s 2 s^{2} ip-llm: Semantic space informed prompt learning with llm for time series forecasting
Z. Pan, Y. Jiang, S. Garg, A. Schneider, Y. Nevmyvaka, and D. Song · 2024
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Test: Text prototype aligned embedding to activate llm’s ability for time series
C. Sun, Y. Li, H. Li, and S. Hong · 2024
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Unified training of universal time series forecasting transformers
G. Woo, C. Liu, A. Kumar, C. Xiong, S. Savarese, and D. Sahoo · 2024
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Beyond forecasting: Compositional time series reasoning for end-to-end task execution, 2024
W. Ye, Y. Zhang, W. Yang, L. Tang, D. Cao, J. Cai, and Y. Liu · 2024
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Rethinking channel dependence for multivariate time series forecasting: Learning from leading indicators
L. Zhao and Y. Shen · 2024
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