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Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare.
Stl: A seasonal-trend decomposition
Robert B Cleveland, William S Cleveland, Jean E McRae, Irma Terpenning, et al. 1990 · 1990
Earlier work this paper cites.
The fractional fourier transform and time-frequency representations
Luis B Almeida. 1994 · 1994
Earlier work this paper cites.
Adapterfusion: Non-destructive task composition for transfer learning
Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, Kyunghyun Cho, and Iryna Gurevych. 2020 · 2005
Earlier work this paper cites.
Scalable semi-automatic annotation for multi-camera person tracking
Jorge Nino, Andrés Frias-Velazquez, Nyan Bo Bo, Maarten Slembrouck, Junzhi Guan, Glen Debard, Bart Vanrumste, Tinne Tuytelaars, and Wilfried Philips. 2016 · 2016
Earlier work this paper cites.
Creating and exploring semantic annotation for behaviour analysis
Kristina Yordanova and Frank Krüger. 2018 · 2018
Earlier work this paper cites.
Semi-automated data labeling for activity recognition in pervasive healthcare
Dagoberto Cruz-Sandoval, Jessica Beltran-Marquez, Matias Garcia-Constantino, Luis A. Gonzalez-Jasso, Jesus Favela, Irvin Hussein Lopez-Nava, Ian Cleland, Andrew Ennis, Netzahualcoyotl Hernandez-Cruz, Joseph Rafferty, Jonathan Synnott, and Chris Nugent. 2019 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Earlier work this paper cites.
Annotation performance for multi-channel time series har dataset in logistics
Christopher Reining, Fernando Moya Rueda, Friedrich Niemann, Gernot A. Fink, and Michael ten Hompel. 2020 · 2020
Earlier work this paper cites.
Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing
Chia-Yu Hsu and Wei-Chen Liu. 2021 · 2021
Earlier work this paper cites.
Interpreting convolutional sequence model by learning local prototypes with adaptation regularization
Jingchao Ni, Zhengzhang Chen, Wei Cheng, Bo Zong, Dongjin Song, Yanchi Liu, Xuchao Zhang, and Haifeng Chen. 2021 · 2021
Earlier work this paper cites.
Voice2series: Reprogramming acoustic models for time series classification
Chao-Han Huck Yang, Yun-Yun Tsai, and Pin-Yu Chen. 2021 · 2021
Earlier work this paper cites.
Informer: Beyond efficient transformer for long sequence time-series forecasting
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021 · 2021
Earlier work this paper cites.
Vlmo: Unified vision-language pre-training with mixture-of-modality-experts
Hangbo Bao, Wenhui Wang, Li Dong, Qiang Liu, Owais Khan Mohammed, Kriti Aggarwal, Subhojit Som, Songhao Piao, and Furu Wei. 2022 · 2022
Earlier work this paper cites.
Pada: Example-based prompt learning for on-the-fly adaptation to unseen domains
Eyal Ben-David, Nadav Oved, and Roi Reichart. 2022 · 2022
Earlier work this paper cites.
Frozen pretrained transformers as universal computation engines
Kevin Lu, Aditya Grover, Pieter Abbeel, and Igor Mordatch. 2022 · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Cited alongside, same era.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023 · 2023
Cited alongside, same era.
Evaluating the feasibility of chatgpt in healthcare: an analysis of multiple clinical and research scenarios
Marco Cascella, Jonathan Montomoli, Valentina Bellini, and Elena Bignami. 2023 · 2023
Cited alongside, same era.
Llm4ts: Aligning pre-trained llms as data-efficient time-series forecasters
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024 · 2024
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Alpacafarm: A simulation framework for methods that learn from human feedback
Yann Dubois, Chen Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy S Liang, and Tatsunori B Hashimoto. 2024 · 2024
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Olmo: Accelerating the science of language models
Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Harsh Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, et al. 2024 · 2024
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Large language models are zero-shot time series forecasters
Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew G Wilson. 2024 · 2024
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Empowering time series analysis with large language models: A survey
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Ching Chang, Wei-Yao Wang, Wen-Chih Peng, and Tien-Fu Chen. 2023 · 2023
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Palm: Scaling language modeling with pathways
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Cited alongside, same era.
Atlas: Few-shot learning with retrieval augmented language models
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2023 · 2023
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Udapter–efficient domain adaptation using adapters
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Timesnet: Temporal 2d-variation modeling for general time series analysis
Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. 2023 · 2023
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Promptcast: A new prompt-based learning paradigm for time series forecasting
Hao Xue and Flora D Salim. 2023 · 2023
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Temporal data meets llm–explainable financial time series forecasting
Xinli Yu, Zheng Chen, Yuan Ling, Shujing Dong, Zongyi Liu, and Yanbin Lu. 2023 · 2023
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Automatic chain of thought prompting in large language models
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Yushan Jiang, Zijie Pan, Xikun Zhang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, and Dongjin Song. 2024 · 2024
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Time-LLM: Time series forecasting by reprogramming large language models
Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen. 2024 · 2024
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A survey of large language models in finance (finllms)
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Unitime: A language-empowered unified model for cross-domain time series forecasting
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Test: Text prototype aligned embedding to activate llm’s ability for time series
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Llm and gnn are complementary: Distilling llm for multimodal graph learning
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One fits all: Power general time series analysis by pretrained lm
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