Fetching the paper…
Reading the bibliography…
Time series data are ubiquitous across diverse real-world applications, making time series analysis critically important.
R. F. Engle, “Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation,” Econometrica: Journal of the econometric society , pp. 987–1007, 1982
1982
Earlier work this paper cites.
J. D. Hamilton, “A new approach to the economic analysis of nonstationary time series and the business cycle,” Econometrica: Journal of the econometric society , pp. 357–384, 1989
1989
Earlier work this paper cites.
A. Asuncion, “Uci machine learning repository,” 2007
2007
Earlier work this paper cites.
S. Akter and S. F. Wamba, “Big data analytics in e-commerce: a systematic review and agenda for future research,” Electronic Markets , vol. 26, no. 2, pp. 173–194, 2016
2016
Earlier work this paper cites.
J.-F. Chen, W.-L. Chen, C.-P. Huang, S.-H. Huang, and A.-P. Chen, “Financial time-series data analysis using deep convolutional neural networks,” in 2016 7th International conference on cloud computing and big data (CCBD) , 2016, pp. 87–92
2016
Earlier work this paper cites.
M. T. Ribeiro, S. Singh, and C. Guestrin, “”why should i trust you?” explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining , 2016, pp. 1135–1144
2016
Earlier work this paper cites.
B. Zhao, H. Lu, S. Chen, J. Liu, and D. Wu, “Convolutional neural networks for time series classification,” Journal of Systems Engineering and Electronics , vol. 28, no. 1, pp. 162–169, 2017
2017
Earlier work this paper cites.
L. Zhu, F. R. Yu, Y. Wang, B. Ning, and T. Tang, “Big data analytics in intelligent transportation systems: A survey,” IEEE Transactions on Intelligent Transportation Systems , vol. 20, no. 1, pp. 383–398, 2018
2018
Earlier work this paper cites.
M. Ge, H. Bangui, and B. Buhnova, “Big data for internet of things: a survey,” Future generation computer systems , vol. 87, pp. 601–614, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) , 2019, pp. 4171–4186
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
H. Ismail Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P.-A. Muller, “Deep learning for time series classification: a review,” Data mining and knowledge discovery , vol. 33, no. 4, pp. 917–963, 2019
2019
Earlier work this paper cites.
H. A. Dau, A. Bagnall, K. Kamgar, C.-C. M. Yeh, Y. Zhu, S. Gharghabi, C. A. Ratanamahatana, and E. Keogh, “The ucr time series archive,” IEEE/CAA Journal of Automatica Sinica , p. 1293–1305, Nov 2019
2019
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” The Journal of Machine Learning Research , vol. 21, no. 1, pp. 5485–5551, 2020
2020
Earlier work this paper cites.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” Advances in Neural Information Processing Systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt, “Measuring massive multitask language understanding,” in International Conference on Learning Representations , 2020
2020
Earlier work this paper cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations , 2020
2020
Earlier work this paper cites.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in Proceedings of the 37th International Conference on Machine Learning (ICML) , vol. 119, 2020, pp. 1597–1607
2020
Earlier work this paper cites.
T. Kim, J. Kim, Y. Tae, C. Park, J.-H. Choi, and J. Choo, “Reversible instance normalization for accurate time-series forecasting against distribution shift,” in International conference on learning representations , 2021
2021
Earlier work this paper cites.
R. Sawhney, A. Wadhwa, S. Agarwal, and R. Shah, “Fast: Financial news and tweet based time aware network for stock trading,” in Proceedings of the 16th conference of the european chapter of the association for computational linguistics: main volume , 2021, pp. 2164–2175
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
H. Bao, L. Dong, S. Piao, and F. Wei, “Beit: Bert pre-training of image transformers,” in International Conference on Learning Representations , 2021
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever, “Learning transferable visual models from natural language supervision,” in Proceedings of the 38th International Conference on Machine Learning (ICML) , ser. Proceedings of Machine Learning Research, vol. 139, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
W. Kim, B. Son, and I. Kim, “Vilt: Vision-and-language transformer without convolution or region supervision,” in International conference on machine learning , 2021, pp. 5583–5594
2021
Earlier work this paper cites.
R. W. Godahewa, C. Bergmeir, G. I. Webb, R. Hyndman, and P. Montero-Manso, “Monash time series forecasting archive,” in Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) , 2021
2021
Earlier work this paper cites.
T. Kim, J. Kim, Y. Tae, C. Park, J.-H. Choi, and J. Choo, “Reversible instance normalization for accurate time-series forecasting against distribution shift,” in International Conference on Learning Representations , 2021
2021
Earlier work this paper cites.
D. Cheng, F. Yang, S. Xiang, and J. Liu, “Financial time series forecasting with multi-modality graph neural network,” Pattern Recognition , vol. 121, p. 108218, 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler et al. , “Emergent abilities of large language models,” Transactions on Machine Learning Research , 2022
2022
Earlier work this paper cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in Neural Information Processing Systems , vol. 35, pp. 24 824–24 837, 2022
2022
Earlier work this paper cites.
K. He, X. Chen, S. Xie, Y. Li, P. Dollar, and R. Girshick, “Masked autoencoders are scalable vision learners,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
J. Li, D. Li, C. Xiong, and S. Hoi, “Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,” in International conference on machine learning . PMLR, 2022, pp. 12 888–12 900
2022
Earlier work this paper cites.
T. Kim, J. Kim, Y. Tae, C. Park, J. Choi, and J. Choo, “Reversible instance normalization for accurate time-series forecasting against distribution shift,” in International Conference on Learning Representations , 2022
2022
Earlier work this paper cites.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen et al. , “Lora: Low-rank adaptation of large language models.” ICLR , vol. 1, no. 2, p. 3, 2022
2022
Earlier work this paper cites.
H. Xue, B. P. Voutharoja, and F. D. Salim, “Leveraging language foundation models for human mobility forecasting,” in Proceedings of the 30th international conference on advances in geographic information systems , 2022, pp. 1–9
2022
Earlier work this paper cites.
V. I. Kontopoulou, A. D. Panagopoulos, I. Kakkos, and G. K. Matsopoulos, “A review of arima vs. machine learning approaches for time series forecasting in data driven networks,” Future Internet , vol. 15, no. 8, p. 255, 2023
2023
Earlier work this paper cites.
F. S. Al-Duais and R. S. Al-Sharpi, “A unique markov chain monte carlo method for forecasting wind power utilizing time series model,” Alexandria Engineering Journal , vol. 74, pp. 51–63, 2023
2023
Earlier work this paper cites.
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann et al. , “Palm: Scaling language modeling with pathways,” Journal of Machine Learning Research , vol. 24, no. 240, pp. 1–113, 2023
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
T. Zhou, P. Niu, L. Sun et al. , “One fits all: Power general time series analysis by pretrained lm,” in Advances in Neural Information Processing Systems , vol. 36, 2023, pp. 43 322–43 355
2023
Earlier work this paper cites.
K. Rasul, A. Ashok, A. R. Williams, A. Khorasani, G. Adamopoulos, R. Bhagwatkar, M. Biloš, H. Ghonia, N. Hassen, A. Schneider et al. , “Lag-llama: Towards foundation models for time series forecasting,” in R0-FoMo: Robustness of Few-shot and Zero-shot Learning in Large Foundation Models , 2023
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
Y. Nie, N. H. Nguyen, P. Sinthong, and J. Kalagnanam, “A time series is worth 64 words: Long-term forecasting with transformers,” in International Conference on Learning Representations , 2023
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” Advances in neural information processing systems , vol. 36, pp. 34 892–34 916, 2023
2023
Earlier work this paper cites.
S. Huang, L. Dong, W. Wang, Y. Hao, S. Singhal, S. Ma, T. Lv, L. Cui, O. K. Mohammed, B. Patra et al. , “Language is not all you need: Aligning perception with language models,” Advances in Neural Information Processing Systems , vol. 36, pp. 72 096–72 109, 2023
2023
Earlier work this paper cites.
S. Dooley, G. S. Khurana, C. Mohapatra et al. , “Forecastpfn: Synthetically-trained zero-shot forecasting,” in Advances in Neural Information Processing Systems , vol. 36, 2023, pp. 2403–2426
2023
Earlier work this paper cites.
C. M. Yeh, X. Dai, H. Chen, Y. Zheng, X. Yan, W. Zhang, S. Wu, L. Wang, and W. Tu, “Toward a foundation model for time series data,” in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM) , 2023, pp. 4400–4404
2023
Earlier work this paper cites.
A. Garza, C. Challu, and M. Mergenthaler-Canseco, “Timegpt-1,” arXiv:2310.03589 , 2023
2023
Earlier work this paper cites.
N. Gruver, M. Finzi, S. Qiu et al. , “Large language models are zero-shot time series forecasters,” in Advances in Neural Information Processing Systems , vol. 36, 2023, pp. 19 622–19 635
2023
Earlier work this paper cites.
H. Xue and F. D. Salim, “Promptcast: A new prompt-based learning paradigm for time series forecasting,” IEEE Transactions on Knowledge and Data Engineering , vol. 36, no. 11, pp. 6851–6864, 2023
2023
Earlier work this paper cites.
C. Chang, W.-C. Peng, and T.-F. Chen, “Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms,” CoRR , 2023
2023
Earlier work this paper cites.
Z. Li, S. Li, and X. Yan, “Time series as images: Vision transformer for irregularly sampled time series,” in Advances in Neural Information Processing Systems , vol. 36, 2023, pp. 49 187–49 204
2023
Cited alongside, same era.
X. Yu, Z. Chen, and Y. Lu, “Harnessing llms for temporal data-a study on explainable financial time series forecasting,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track , 2023, pp. 739–753
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2024
Closest in time.
Z. Dong, R. Li, Y. Wu et al. , “Brain-jepa: Brain dynamics foundation model with gradient positioning and spatiotemporal masking,” in Advances in Neural Information Processing Systems , vol. 37, 2024, pp. 86 048–86 073
2024
Closest in time.
D. Zhang, Z. Yuan, J. Chen, K. Chen, and Y. Yang, “Brant-x: A unified physiological signal alignment framework,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2024, pp. 4155–4166
2024
Closest in time.
C. Liu, Z. Wan, C. Ouyang, A. Shah, W. Bai, and R. Arcucci, “Zero-shot ecg classification with multimodal learning and test-time clinical knowledge enhancement,” in Proceedings of the 41st International Conference on Machine Learning (ICML) , vol. 235, 2024, pp. 31 949–31 963
2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2023
Cited alongside, same era.
X. Yu, Z. Chen, and Y. Lu, “Harnessing llms for temporal data-a study on explainable financial time series forecasting,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track , 2023, pp. 739–753
2023
Cited alongside, same era.
2023
Cited alongside, same era.
D. Zhang, Z. Yuan, Y. Yang et al. , “Brant: Foundation model for intracranial neural signal,” in Advances in Neural Information Processing Systems , vol. 36, 2023, pp. 26 304–26 321
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
T. Nguyen, J. Brandstetter, A. Kapoor et al. , “Climax: A foundation model for weather and climate,” in Proceedings of the 40th International Conference on Machine Learning , 2023, pp. 25 904–25 938
2023
Cited alongside, same era.
H. Chen and H. Eldardiry, “Graph time-series modeling in deep learning: a survey,” ACM Transactions on Knowledge Discovery from Data , vol. 18, no. 5, pp. 1–35, 2024
2024
Cited alongside, same era.
Closest in time.
2024
Closest in time.
Y. Yuan, J. Ding, J. Feng, D. Jin, and Y. Li, “Unist: A prompt-empowered universal model for urban spatio-temporal prediction,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2024, pp. 4095–4106
2024
Closest in time.
Z. Li, L. Xia, J. Tang, Y. Xu, L. Shi, L. Xia, D. Yin, and C. Huang, “Urbangpt: Spatio-temporal large language models,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2024, pp. 5351–5362
2024
Closest in time.
2024
Closest in time.
C. Liu, S. Yang, Q. Xu, Z. Li, C. Long, Z. Li, and R. Zhao, “Spatial-temporal large language model for traffic prediction,” in 2024 25th IEEE International Conference on Mobile Data Management (MDM) , 2024, pp. 31–40
2024
Closest in time.
Z. Zhang, H. Amiri, Z. Liu, L. Zhao, and A. Züfle, “Large language models for spatial trajectory patterns mining,” in Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection , 2024, pp. 52–55
2024
Closest in time.
Z. Lai, C. Yang, S. Lan, L. Wang, W. Shen, and L. Zhu, “Bearingfm: Towards a foundation model for bearing fault diagnosis by domain knowledge and contrastive learning,” International Journal of Production Economics , vol. 275, p. 109319, 2024
2024
Closest in time.
X. Zhang, D. Teng, R. R. Chowdhury et al. , “Unimts: Unified pre-training for motion time series,” in Advances in Neural Information Processing Systems , vol. 37, 2024, pp. 107 469–107 493
2024
Closest in time.
Y. Sarrof, Y. Veitsman, and M. Hahn, “The expressive capacity of state space models: A formal language perspective,” Advances in Neural Information Processing Systems , vol. 37, pp. 41 202–41 241, 2024
2024
Closest in time.
C. Ravuru, S. S. Sakhinana, and V. Runkana, “Agentic retrieval-augmented generation for time series analysis,” Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) , 2024
2024
Closest in time.
J. Cheng and P. Chin, “Sociodojo: Building lifelong analytical agents with real-world text and time series,” in International Conference on Learning Representations (ICLR) , 2024
2024
Closest in time.
M. Lin, Z. Chen, Y. Liu, X. Zhao, Z. Wu, J. Wang, X. Zhang, S. Wang, and H. Chen, “Decoding time series with llms: A multi-agent framework for cross-domain annotation,” arXiv preprint , 2024
2024
Closest in time.
2024
Closest in time.
W. Chow, L. E. Gardiner, H. T. Hallgrimsson, M. A. Xu, and S. Y. Ren, “Towards time-series reasoning with llms,” in Proceedings of the NeurIPS 2024 Workshop on Time Series in the Age of Large Models , 2024
2024
Closest in time.
M. Tan, M. Merrill, V. Gupta et al. , “Are language models actually useful for time series forecasting?” in Advances in Neural Information Processing Systems , vol. 37, 2024, pp. 60 162–60 191
2024
Closest in time.
X. Kong, Z. Chen, W. Liu, K. Ning, L. Zhang, S. Muhammad Marier, Y. Liu, Y. Chen, and F. Xia, “Deep learning for time series forecasting: a survey,” International Journal of Machine Learning and Cybernetics , pp. 1–34, 2025
2025
Closest in time.
2025
Closest in time.
J. Chen, J. Yang, H. Wu, D. Li, J. Gao, T. Zhou, and B. Xiao, “Florence-vl: Enhancing vision-language models with generative vision encoder and depth-breadth fusion,” in Proceedings of the Computer Vision and Pattern Recognition Conference , 2025, pp. 24 928–24 938
2025
Closest in time.
2025
Closest in time.
M. Wang, T. Ma, and S. B. Cohen, “Pre-training time series models with stock data customization,” in Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2 , 2025, pp. 3019–3030
2025
Closest in time.
G. Lee, W. Yu, K. Shin, W. Cheng, and H. Chen, “Timecap: Learning to contextualize, augment, and predict time series events with large language model agents,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 39, no. 17, 2025, pp. 18 082–18 090
2025
Closest in time.
C. Wang, Q. Qi, J. Wang, H. Sun, Z. Zhuang, J. Wu, L. Zhang, and J. Liao, “Chattime: A unified multimodal time series foundation model bridging numerical and textual data,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 39, no. 12, 2025, pp. 12 694–12 702
2025
Closest in time.
X. Shi, S. Wang, Y. Nie, D. Li, Z. Ye, Q. Wen, and M. Jin, “Time-moe: Billion-scale time series foundation models with mixture of experts,” in International Conference on Learning Representations (ICLR) , 2025
2025
Closest in time.
Y. Liu, G. Qin, Z. Shi, Z. Chen, C. Yang, X. Huang, J. Wang, and M. Long, “Sundial: A family of highly capable time series foundation models,” in International Conference on Machine Learning (ICML) , 2025
2025
Closest in time.
S. Chen, G. Long, J. Jiang et al. , “Federated foundation models on heterogeneous time series,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 39, no. 15, 2025, pp. 15 839–15 847
2025
Closest in time.
H. Zhang, C. Xu, Y.-F. Zhang, Z. Zhang, L. Wang, and J. Bian, “Timeraf: Retrieval-augmented foundation model for zero-shot time series forecasting,” IEEE Transactions on Knowledge & Data Engineering , no. 01, pp. 1–12, 2025
2025
Closest in time.
Y. Wang, Y. Qiu, P. Chen, K. Zhao, Y. Shu, Z. Rao, L. Pan, B. Yang, and C. Guo, “Towards a general time series forecasting model with unified representation and adaptive transfer,” in Forty-second International Conference on Machine Learning , 2025
2025
Closest in time.
X. Liu, J. Liu, G. Woo, T. Aksu, Y. Liang, R. Zimmermann, C. Liu, S. Savarese, C. Xiong, and D. Sahoo, “Moirai-moe: Empowering time series foundation models with sparse mixture of experts,” in International Conference on Machine Learning (ICML) , 2025
2025
Closest in time.
A. Das, M. Faw et al. , “In-context fine-tuning for time-series foundation models,” in International Conference on Machine Learning (ICML) , 2025
2025
Closest in time.
Q. Yao, C.-H. H. Yang, R. Jiang, Y. Liang, M. Jin, and S. Pan, “Towards neural scaling laws for time series foundation models,” in The Thirteenth International Conference on Learning Representations (ICLR 2025) , 2025
2025
Closest in time.
P. Liu, H. Guo, T. Dai et al. , “Calf: Aligning llms for time series forecasting via cross-modal fine-tuning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 39, no. 18, 2025, pp. 18 915–18 923
2025
Closest in time.
W. Zhang, C. Yin, H. Liu, and H. Xiong, “Unleashing the power of pre-trained language models for irregularly sampled time series,” in Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2 , 2025, pp. 3831–3842
2025
Closest in time.
Y. Jiexia, W. Zhang, Z. Li, J. Li, and F. Tsung, “Dualtime: A dual-adapter language model for time series multimodal representation learning,” in International Joint Conference on Artificial Intelligence (IJCAI) , 2025
2025
Closest in time.
Q. Huang, Z. Zhou, K. Yang et al. , “Exploiting language power for time series forecasting with exogenous variables,” in Proceedings of the ACM Web Conference 2025 , 2025, pp. 4043–4052
2025
Closest in time.
M. Cheng, Y. Chen, Q. Liu et al. , “Instructime: Advancing time series classification with multimodal language modeling,” in Proceedings of the 18th ACM International Conference on Web Search and Data Mining (WSDM) , 2025, pp. 792–800
2025
Closest in time.
Y. Hu, Q. Li, D. Zhang, J. Yan, and Y. Chen, “Context-alignment: Activating and enhancing llms capabilities in time series,” in International Conference on Learning Representations (ICLR) , 2025
2025
Closest in time.
Z. Zhao, P. Wang, H. Wen, S. Wang, L. Yu, and Y. Wang, “Stem-lts: Integrating semantic-temporal dynamics in llm-driven time series analysis,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 39, no. 21, 2025, pp. 22 858–22 866
2025
Closest in time.
W. Niu, Z. Xie, Y. Sun, W. He, M. Xu, and C. Hao, “Langtime: A language-guided unified model for time series forecasting with proximal policy optimization,” in International Conference on Machine Learning (ICML) , 2025
2025
Closest in time.
2025
Closest in time.
M. Chen, L. Shen, Z. Li, X. J. Wang, J. Sun, and C. Liu, “Visionts: Visual masked autoencoders are free-lunch zero-shot time series forecasters,” in Proceedings of the 42nd International Conference on Machine Learning (ICML) , 2025
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
S. Zhong, W. Ruan, M. Jin, H. Li, Q. Wen, and Y. Liang, “Time-vlm: Exploring multimodal vision-language models for augmented time series forecasting,” in International Conference on Machine Learning (ICML), Poster , 2025
2025
Closest in time.
Y. Zhang, W. Yang, J. Wang, Q. Ma, and J. Xiong, “Camef: Causal-augmented multi-modality event-driven financial forecasting by integrating time series patterns and salient macroeconomic announcements,” in Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2 , 2025, pp. 3867–3878
2025
Closest in time.
Y. JIANG, Y. Chen, X. Li, Q. Chao, S. LIU, and G. Cong, “FSTLLM: Spatio-temporal LLM for few shot time series forecasting,” in Forty-second International Conference on Machine Learning(ICML) , 2025
2025
Closest in time.
H. Li, Y. Huang, C. Xu, V. Schlegel, R. Jiang, R. Batista-Navarro, G. Nenadic, and J. Bian, “Bridge: Bootstrapping text to control time-series generation via multi-agent iterative optimization and diffusion modelling,” in International Conference on Machine Learning (ICML) , 2025
2025
Closest in time.
Z. Xie, Z. Li, X. He, L. Xu, X. Wen, T. Zhang, J. Chen, R. Shi, and D. Pei, “Chatts: Aligning time series with llms via synthetic data for enhanced understanding and reasoning,” Proceedings of the VLDB Endowment , vol. 18, no. 8, pp. 2385–2398, 2025
2025
Closest in time.