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Reasoning ability is crucial for solving challenging tasks.
Language models are few-shot learners
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Financial time series forecasting with deep learning: A systematic literature review: 2005–2019
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Causal inference for time series analysis: Problems, methods and evaluation
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Actionable Insights in Urban Multivariate Time-series. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 1774–1783
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Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
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Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence , Vol. 35. 11106–11115
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Training a helpful and harmless assistant with reinforcement learning from human feedback
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Training language models to follow instructions with human feedback
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Self-consistency improves chain of thought reasoning in language models
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Chain-of-thought prompting elicits reasoning in large language models
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TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. In The Eleventh International Conference on Learning Representations
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Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al · 2023
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Large language models are zero-shot time series forecasters
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What can large language models do in chemistry? a comprehensive benchmark on eight tasks
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Large Pre-trained time series models for cross-domain Time series analysis tasks
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Reward design with language models
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Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation
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Criticbench: Benchmarking llms for critique-correct reasoning
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A picture is worth a thousand numbers: Enabling llms reason about time series via visualization
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LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting. In Findings of the Association for Computational Linguistics ACL 2024 . 7832–7840
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itransformer: Inverted transformers are effective for time series forecasting
Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, and Mingsheng Long. 2023 · 2023
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Self-refine: Iterative refinement with self-feedback
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A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. In International Conference on Learning Representations
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Liangming Pan, Michael Saxon, Wenda Xu, Deepak Nathani, Xinyi Wang, and William Yang Wang. 2023 · 2023
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Causal inference for time series
Jakob Runge, Andreas Gerhardus, Gherardo Varando, Veronika Eyring, and Gustau Camps-Valls. 2023 · 2023
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Llm-planner: Few-shot grounded planning for embodied agents with large language models. In Proceedings of the IEEE/CVF international conference on computer vision . 2998–3009
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Adaplanner: Adaptive planning from feedback with language models
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Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations
Sarabeth M Mathis, Alexander E Webber, Tomás M León, Erin L Murray, Monica Sun, Lauren A White, Logan C Brooks, Alden Green, Addison J Hu, Roni Rosenfeld, et al · 2024
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Reasoning with large language models, a survey
Aske Plaat, Annie Wong, Suzan Verberne, Joost Broekens, Niki van Stein, and Thomas Back. 2024 · 2024
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Machine learning for data-centric epidemic forecasting
Alexander Rodríguez, Harshavardhan Kamarthi, Pulak Agarwal, Javen Ho, Mira Patel, Suchet Sapre, and B Aditya Prakash. 2024 · 2024
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al · 2024
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Time-moe: Billion-scale time series foundation models with mixture of experts
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Jiahao Wang, Mingyue Cheng, Qingyang Mao, Qi Liu, Feiyang Xu, Xin Li, and Enhong Chen. 2024a · 2024
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Deep time series models: A comprehensive survey and benchmark
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Unified Training of Universal Time Series Forecasting Transformers
Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, and Doyen Sahoo. 2024 · 2024
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A survey on knowledge distillation of large language models
Xiaohan Xu, Ming Li, Chongyang Tao, Tao Shen, Reynold Cheng, Jinyang Li, Can Xu, Dacheng Tao, and Tianyi Zhou. 2024 · 2024
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Tiny time mixers (ttms): Fast pre-trained models for enhanced zero/few-shot forecasting of multivariate time series
Vijay Ekambaram, Arindam Jati, Pankaj Dayama, Sumanta Mukherjee, Nam Nguyen, Wesley M Gifford, Chandra Reddy, and Jayant Kalagnanam. 2025 · 2025
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
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Context-Alignment: Activating and Enhancing LLMs Capabilities in Time Series. In The Thirteenth International Conference on Learning Representations
Yuxiao Hu, Qian Li, Jinyue Yan, Dongxiao Zhang, and Yuntian Chen. 2025 · 2025
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Time-mmd: Multi-domain multimodal dataset for time series analysis
Haoxin Liu, Shangqing Xu, Zhiyuan Zhao, Lingkai Kong, Harshavardhan Prabhakar Kamarthi, Aditya Sasanur, Megha Sharma, Jiaming Cui, Qingsong Wen, Chao Zhang, et al · 2025
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Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
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Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning
Yibo Yan, Shen Wang, Jiahao Huo, Jingheng Ye, Zhendong Chu, Xuming Hu, Philip S Yu, Carla Gomes, Bart Selman, and Qingsong Wen. 2025 · 2025
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A Benchmark and Chain-of-Thought Prompting Strategy for Large Multimodal Models with Multiple Image Inputs. In International Conference on Pattern Recognition . Springer, 226–241
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Isr-llm: Iterative self-refined large language model for long-horizon sequential task planning. In 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2081–2088
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