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Recently, there has been a growing interest in leveraging pre-trained large language models (LLMs) for various time series applications.
The interpolation of time series by related series
Friedman, M · 1962
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Time-series. 2nd edn
Anderson, O. D. and Kendall, M. G · 1976
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Stl: A seasonal-trend decomposition
Cleveland, R. B., Cleveland, W. S., McRae, J. E., and Terpenning, I · 1990
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Timing of seasonal sales
Courty, P. and Li, H · 1999
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Probabilistic demand forecasting at scale
Böse, J.-H., Flunkert, V., Gasthaus, J., Januschowski, T., Lange, D., Salinas, D., Schelter, S., Seeger, M., and Wang, Y · 2017
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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Li, Y., Yu, R., Shahabi, C., and Liu, Y · 2017
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A dual-stage attention-based recurrent neural network for time series prediction
Qin, Y., Song, D., Cheng, H., Cheng, W., Jiang, G., and Cottrell, G. W · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Transfer learning for time series classification
Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., and Muller, P.-A · 2018
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Modeling long-and short-term temporal patterns with deep neural networks
Lai, G., Chang, W.-C., Yang, Y., and Liu, H · 2018
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The m4 competition: Results, findings, conclusion and way forward
Makridakis, S., Spiliotis, E., and Assimakopoulos, V · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al · 2018
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Forecasting at scale
Taylor, S. J. and Letham, B · 2018
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Ethayarajh, K · 2019
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Parameter-efficient transfer learning for nlp
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S · 2019
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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
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 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
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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
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Spectral temporal graph neural network for multivariate time-series forecasting
Cao, D., Wang, Y., Duan, J., Zhang, C., Zhu, X., Huang, C., Tong, Y., Xu, B., Bai, J., Tong, J., et al · 2020
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Time series analysis of climate variables using seasonal arima approach
Dimri, T., Ahmad, S., and Sharif, M · 2020
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Robusttad: Robust time series anomaly detection via decomposition and convolutional neural networks
Gao, J., Song, X., Wen, Q., Wang, P., Sun, L., and Xu, H · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
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Connecting the dots: Multivariate time series forecasting with graph neural networks
Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., and Zhang, C · 2020
Cited alongside, same era.
Beit: Bert pre-training of image transformers
Bao, H., Dong, L., Piao, S., and Wei, F · 2021
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Efficiently modeling long sequences with structured state spaces
Gu, A., Goel, K., and Ré, C · 2021
Gpt-4 technical report
Achiam, O. J. and et al., S. A · 2023
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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 · 2023
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Nhits: Neural hierarchical interpolation for time series forecasting
Challu, C., Olivares, K. G., Oreshkin, B. N., Ramirez, F. G., Canseco, M. M., and Dubrawski, A · 2023
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Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms
Chang, C., Peng, W.-C., and Chen, T.-F · 2023
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Garza, A. and Mergenthaler-Canseco, M · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Cited alongside, same era.
Reversible instance normalization for accurate time-series forecasting against distribution shift
Kim, T., Kim, J., Tae, Y., Park, C., Choi, J.-H., and Choo, J · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 2021
Cited alongside, same era.
Discrete graph structure learning for forecasting multiple time series
Shang, C., Chen, J., and Bi, J · 2021
Cited alongside, same era.
Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Wu, H., Xu, J., Wang, J., and Long, M · 2021
Cited alongside, same era.
Informer: Beyond efficient transformer for long sequence time-series forecasting
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W · 2021
Cited alongside, same era.
Deldari, S., Xue, H., Saeed, A., He, J., Smith, D. V., and Salim, F. D · 2022
Cited alongside, same era.
Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2022
Cited alongside, same era.
Large language models in finance: A survey
Li, Y., Wang, S., Ding, H., and Chen, H · 2023
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Sadi: A self-adaptive decomposed interpretable framework for electric load forecasting under extreme events
Liu, H., Ma, Z., Yang, L., Zhou, T., Xia, R., Wang, Y., Wen, Q., and Sun, L · 2023
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Large Language Models Are Zero Shot Time Series Forecasters
Nate Gruver, Marc Finzi, S. Q. and Wilson, A. G · 2023
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A time series is worth 64 words: Long-term forecasting with transformers
Nie, Y., H. Nguyen, N., Sinthong, P., and Kalagnanam, J · 2023
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Lag-llama: Towards foundation models for time series forecasting
Rasul, K., Ashok, A., Williams, A. R., Khorasani, A., Adamopoulos, G., Bhagwatkar, R., Biloš, M., Ghonia, H., Hassen, N. V., Schneider, A., et al · 2023
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Test: Text prototype aligned embedding to activate llm’s ability for time series
Sun, C., Li, Y., Li, H., and Hong, S · 2023
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One Fits All: Power general time series analysis by pretrained lm
Tian Zhou, Peisong Niu, X. W. L. S. and Jin, R · 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
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A survey on multimodal large language models
Yin, S., Fu, C., Zhao, S., Li, K., Sun, X., Xu, T., and Chen, E · 2023
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Are transformers effective for time series forecasting?
Zeng, A., Chen, M., Zhang, L., and Xu, Q · 2023
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Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., and Jin, R · 2023
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A survey on multimodal large language models for autonomous driving
Cui, C., Ma, Y., Cao, X., Ye, W., Zhou, Y., Liang, K., Chen, J., Lu, J., Yang, Z., Liao, K.-D., et al · 2024
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Empowering time series analysis with large language models: A survey
Jiang, Y., Pan, Z., Zhang, X., Garg, S., Schneider, A., Nevmyvaka, Y., and Song, D · 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
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Structural knowledge informed continual multivariate time series forecasting
Pan, Z., Jiang, Y., Song, D., Garg, S., Rasul, K., Schneider, A., and Nevmyvaka, Y · 2024
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