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Time-Series Mining (TSM) is an important research area since it shows great potential in practical applications.
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Y. Ozyurt, S. Feuerriegel, and C. Zhang, “Contrastive learning for unsupervised domain adaptation of time series,” in The Eleventh International Conference on Learning Representations , 2023, pp. 1–40
2023
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E. Eldele, M. Ragab, Z. Chen, M. Wu, C.-K. Kwoh, and X. Li, “Label-efficient time series representation learning: A review,” IEEE Transactions on Artificial Intelligence , pp. 1–16, 2024
2024
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K. Zhang, Q. Wen, C. Zhang, R. Cai, M. Jin, Y. Liu, J. Y. Zhang, Y. Liang, G. Pang, D. Song, and S. Pan, “Self-supervised learning for time series analysis: Taxonomy, progress, and prospects,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 46, no. 10, pp. 6775–6794, 2024
2024
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M. Jin, Y. Zhang, W. Chen, K. Zhang, Y. Liang, B. Yang, J. Wang, S. Pan, and Q. Wen, “Position paper: What can large language models tell us about time series analysis,” in In Forty-first International Conference on Machine Learning , 2024, pp. 1–17
2024
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Y. Liang, H. Wen, Y. Nie, Y. Jiang, M. Jin, D. Song, S. Pan, and Q. Wen, “Foundation models for time series analysis: A tutorial and survey,” in In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2024, p. 6555–6565
2024
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Q. Xu, K. Wu, M. Wu, K. Mao, X. Li, and Z. Chen, “Reinforced knowledge distillation for time series regression,” IEEE Transactions on Artificial Intelligence , vol. 5, no. 6, pp. 3184–3194, 2024
2024
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Y. Wang, Y. Xu, J. Yang, M. Wu, X. Li, L. Xie, and Z. Chen, “Graph-aware contrasting for multivariate time-series classification,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 14, 2024, pp. 15 725–15 734
2024
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D. Cao, F. Jia, S. O. Arik, T. Pfister, Y. Zheng, W. Ye, and Y. Liu, “Tempo: Prompt-based generative pre-trained transformer for time series forecasting,” in The Twelfth International Conference on Learning Representations , 2024, pp. 1–33
2024
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M. Jin, S. Wang, L. Ma, Z. Chu, J. Y. Zhang, X. Shi, P.-Y. Chen, Y. Liang, Y.-F. Li, S. Pan et al. , “Time-llm: Time series forecasting by reprogramming large language models,” in The Twelfth International Conference on Learning Representations , 2024, pp. 1–24
2024
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Y. Liu, H. Zhang, C. Li, X. Huang, J. Wang, and M. Long, “Timer: Generative pre-trained transformers are large time series models,” in Forty-first International Conference on Machine Learning , 2024, pp. 1–31
2024
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G. Woo, C. Liu, A. Kumar, C. Xiong, S. Savarese, and D. Sahoo, “Unified training of universal time series forecasting transformers,” in Forty-first International Conference on Machine Learning , 2024, pp. 1–25
2024
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A. Das, W. Kong, R. Sen, and Y. Zhou, “A decoder-only foundation model for time-series forecasting,” in Forty-first International Conference on Machine Learning , 2024, pp. 1–21
2024
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J. Dong, H. Wu, Y. Wang, Y. Qiu, L. Zhang, J. Wang, and M. Long, “Timesiam: A pre-training framework for siamese time-series modeling,” in Forty-first International Conference on Machine Learning , 2024, pp. 1–25
2024
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E. Eldele, M. Ragab, Z. Chen, M. Wu, and X. Li, “Tslanet: Rethinking transformers for time series representation learning,” in Forty-first International Conference on Machine Learning , 2024, pp. 1–20
2024
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Y. Zhang, M. Liu, S. Zhou, and J. Yan, “Up2me: Univariate pre-training to multivariate fine-tuning as a general-purpose framework for multivariate time series analysis,” in Forty-first International Conference on Machine Learning , 2024, pp. 1–24
2024
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M. Goswami, K. Szafer, A. Choudhry, Y. Cai, S. Li, and A. Dubrawski, “Moment: A family of open time-series foundation models,” in Forty-first International Conference on Machine Learning , 2024, pp. 1–38
2024
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L. Hou, Y. Geng, L. Han, H. Yang, K. Zheng, and X. Wang, “Masked token enabled pre-training: A task-agnostic approach for understanding complex traffic flow,” IEEE Transactions on Mobile Computing , pp. 1–12, 2024
2024
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J. Liu and S. Chen, “Timesurl: Self-supervised contrastive learning for universal time series representation learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 12, 2024, pp. 13 918–13 926
2024
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Y. Liu, T. Hu, H. Zhang, H. Wu, S. Wang, L. Ma, and M. Long, “itransformer: Inverted transformers are effective for time series forecasting,” in The Twelfth International Conference on Learning Representations , 2024, pp. 1–25
2024
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D. Luo and X. Wang, “Moderntcn: A modern pure convolution structure for general time series analysis,” in The Twelfth International Conference on Learning Representations , 2024, pp. 1–43
2024
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L. Zhu, B. Liao, Q. Zhang, X. Wang, W. Liu, and X. Wang, “Vision mamba: Efficient visual representation learning with bidirectional state space model,” in Forty-first International Conference on Machine Learning , 2024, pp. 1–11
2024
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2024
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2024
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2024
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Z. Liu, W. Pei, D. Lan, and Q. Ma, “Diffusion language-shapelets for semi-supervised time-series classification,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 13, 2024, pp. 14 079–14 087
2024
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