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Multivariate time-series forecasting is vital in various domains, e.g., economic planning and weather prediction.
Modeling long- and short-term temporal patterns with deep neural networks
Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu · 2018
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Transfer learning for financial time series forecasting
Qi-Qiao He, Patrick Cheong-Iao Pang, and Yain-Whar Si · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Interpretable multi-task learning for product quality prediction with attention mechanism
Cheng-Han Yeh, Yao-Chung Fan, and Wen-Chih Peng · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Product quality prediction with convolutional encoder-decoder architecture and transfer learning
Hao-Yi Chih, Yao-Chung Fan, Wen-Chih Peng, and Hai-Yuan Kuo · 2020
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Multi-source transfer learning with ensemble for financial time series forecasting
Qi-Qiao He, Patrick Cheong-Iao Pang, and Yain-Whar Si · 2020
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Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station
Pradeep Hewage, Ardhendu Behera, Marcello Trovati, Ella Pereira, Morteza Ghahremani, Francesco Palmieri, and Yonghuai Liu · 2020
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Stock price prediction using time series, econometric, machine learning, and deep learning models
Ananda Chatterjee, Hrisav Bhowmick, and Jaydip Sen · 2021
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Time-series representation learning via temporal and contextual contrasting
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, and Cuntai Guan · 2021
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Time-series forecasting with deep learning: a survey
Bryan Lim and Stefan Zohren · 2021
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Gated transformer networks for multivariate time series classification
Minghao Liu, Shengqi Ren, Siyuan Ma, Jiahui Jiao, Yizhou Chen, Zhiguang Wang, and Wei Song · 2021
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Unsupervised representation learning for time series with temporal neighborhood coding
Sana Tonekaboni, Danny Eytan, and Anna Goldenberg · 2021
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Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long · 2021
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When do you need billions of words of pretraining data?
Yian Zhang, Alex Warstadt, Xiaocheng Li, and Samuel R. Bowman · 2021
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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
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A comparative study of demand forecasting based on machine learning methods with time series approach
Akbar Abbaspour Ghadim Bonab · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre · 2022
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Lora: Low-rank adaptation of large language models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
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Reversible instance normalization for accurate time-series forecasting against distribution shift
Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, and Jaegul Choo · 2022
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Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Matthew Jones, Tengyu Ma, and Percy Liang · 2022
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Frozen pretrained transformers as universal computation engines
Kevin Lu, Aditya Grover, Pieter Abbeel, and Igor Mordatch · 2022
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Coronavirus (covid-19): Arima-based time-series analysis to forecast near future and the effect of school reopening in india
Hiteshi Tandon, Prabhat Ranjan, Tanmoy Chakraborty, and Vandana Suhag · 2022
Cited alongside, same era.
Unsupervised representation learning for time series: A review
Qianwen Meng, Hangwei Qian, Yong Liu, Yonghui Xu, Zhiqi Shen, and Lizhen Cui · 2023
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A time series is worth 64 words: Long-term forecasting with transformers
Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam · 2023
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Audio-visual LLM for video understanding
Fangxun Shu, Lei Zhang, Hao Jiang, and Cihang Xie · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus · 2022
Cited alongside, same era.
Anomaly transformer: Time series anomaly detection with association discrepancy
Jiehui Xu, Haixu Wu, Jianmin Wang, and Mingsheng Long · 2022
Cited alongside, same era.
Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion
Ling Yang and Shenda Hong · 2022
Cited alongside, same era.
Ts2vec: Towards universal representation of time series
Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu · 2022
Cited alongside, same era.
Self-supervised contrastive pre-training for time series via time-frequency consistency
Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, and Marinka Zitnik · 2022
Cited alongside, same era.
Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin · 2022
Cited alongside, same era.
Transformers in time-series analysis: A tutorial
Sabeen Ahmed, Ian E. Nielsen, Aakash Tripathi, Shamoon Siddiqui, Ravi Prakash Ramachandran, and Ghulam Rasool · 2023
Cited alongside, same era.
Alexandros-Menelaos Tzortzis, Sotiris Pelekis, Evangelos Spiliotis, Spiros Mouzakitis, John E. Psarras, and Dimitris Askounis · 2023
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Transformers in time series: A survey
Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun · 2023
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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
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Are transformers effective for time series forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu · 2023
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Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting
Yunhao Zhang and Junchi Yan · 2023
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Simts: Rethinking contrastive representation learning for time series forecasting
Xiaochen Zheng, Xingyu Chen, Manuel Schürch, Amina Mollaysa, Ahmed Allam, and Michael Krauthammer · 2023
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One fits all: Power general time series analysis by pretrained LM
Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, and Rong Jin · 2023
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TEMPO: Prompt-based generative pre-trained transformer for time series forecasting
Defu Cao, Furong Jia, Sercan O Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, and Yan Liu · 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
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Ts-fastformer: Fast transformer for time-series forecasting
Sangwon Lee, Junho Hong, Ling Liu, and Wonik Choi · 2024
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Table meets llm: Can large language models understand structured table data? a benchmark and empirical study
Yuan Sui, Mengyu Zhou, Mingjie Zhou, Shi Han, and Dongmei Zhang · 2024
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TEST: Text prototype aligned embedding to activate LLM’s ability for time series
Chenxi Sun, Hongyan Li, Yaliang Li, and Shenda Hong · 2024
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Frequency-domain mlps are more effective learners in time series forecasting
Kun Yi, Qi Zhang, Wei Fan, Shoujin Wang, Pengyang Wang, Hui He, Ning An, Defu Lian, Longbing Cao, and Zhendong Niu · 2024
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