Fetching the paper…
Reading the bibliography…
Time series forecasting plays a vital role in supporting decision-making across a wide range of critical applications, including energy, healthcare, and finance.
Forecasting sales by exponentially weighted moving averages
Peter R Winters · 1960
Earlier work this paper cites.
Forecasting seasonals and trends by exponentially weighted moving averages
Charles C Holt · 2004
Earlier work this paper cites.
Time series forecasting using holt-winters exponential smoothing
Prajakta S Kalekar et al · 2004
Earlier work this paper cites.
Experiencing sax: a novel symbolic representation of time series
Jessica Lin, Eamonn Keogh, Li Wei, and Stefano Lonardi · 2007
Earlier work this paper cites.
Automatic time series forecasting: the forecast package for r
Rob J Hyndman and Yeasmin Khandakar · 2008
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
Earlier work this paper cites.
Fred-md: A monthly database for macroeconomic research
Michael W McCracken and Serena Ng · 2016
Earlier work this paper cites.
Modeling long-and short-term temporal patterns with deep neural networks
Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu · 2018
Earlier work this paper cites.
Temporal relational ranking for stock prediction
Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, and Tat-Seng Chua · 2019
Earlier work this paper cites.
Probabilistic forecasting with spline quantile function rnns
Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, and Tim Januschowski · 2019
Earlier work this paper cites.
Deep factors for forecasting
Yuyang Wang, Alex Smola, Danielle Maddix, Jan Gasthaus, Dean Foster, and Tim Januschowski · 2019
Earlier work this paper cites.
Global transpiration data from sap flow measurements: the sapfluxnet database
Rafael Poyatos, Víctor Granda, Víctor Flo, Mark A Adams, Balázs Adorján, David Aguadé, Marcos PM Aidar, Scott Allen, M Susana Alvarado-Barrientos, Kristina J Anderson-Teixeira, et al · 2020
Earlier work this paper cites.
Deepar: Probabilistic forecasting with autoregressive recurrent networks
David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski · 2020
Earlier work this paper cites.
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 · 2021
Earlier work this paper cites.
Temporal fusion transformers for interpretable multi-horizon time series forecasting
Bryan Lim, Sercan Ö Arık, Nicolas Loeff, and Tomas Pfister · 2021
Cited alongside, same era.
Transfer graph neural networks for pandemic forecasting
George Panagopoulos, Giannis Nikolentzos, and Michalis Vazirgiannis · 2021
Cited alongside, same era.
Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long · 2021
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.
Simmtm: A simple pre-training framework for masked time-series modeling
Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jianmin Wang, and Mingsheng Long · 2023
Cited alongside, same era.
Lstprompt: Large language models as zero-shot time series forecasters by long-short-term prompting
Haoxin Liu, Zhiyuan Zhao, Jindong Wang, Harshavardhan Kamarthi, and B Aditya Prakash · 2024
Later among the works it cites.
Tfb: Towards comprehensive and fair benchmarking of time series forecasting methods
Xiangfei Qiu, Jilin Hu, Lekui Zhou, Xingjian Wu, Junyang Du, Buang Zhang, Chenjuan Guo, Aoying Zhou, Christian S Jensen, Zhenli Sheng, et al · 2024
Later among the works it cites.
Test: Text prototype aligned embedding to activate llm’s ability for time series
Chenxi Sun, Hongyan Li, Yaliang Li, and Shenda Hong · 2024
Later among the works it cites.
Timemixer: Decomposable multiscale mixing for time series forecasting
Shiyu Wang, Haixu Wu, Xiaoming Shi, Tengge Hu, Huakun Luo, Lintao Ma, James Zhang, and Jun Zhou · 2024
Later among the works it cites.
Llm4ts: Aligning pre-trained llms as data-efficient time-series forecasters
Ching Chang, Wei-Yao Wang, Wen-Chih Peng, and Tien-Fu Chen · 2025
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Huiqiang Wang, Jian Peng, Feihu Huang, Jince Wang, Junhui Chen, and Yifei Xiao · 2023
Cited alongside, same era.
Promptcast: A new prompt-based learning paradigm for time series forecasting
Hao Xue and Flora D Salim · 2023
Cited alongside, same era.
Are transformers effective for time series forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu · 2023
Cited alongside, same era.
Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting
Yunhao Zhang and Junchi Yan · 2023
Cited alongside, same era.
A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
Cited alongside, same era.
One fits all: Power general time series analysis by pretrained lm
Tian Zhou, Peisong Niu, Liang Sun, Rong Jin, et al · 2023
Cited alongside, same era.
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
Cited alongside, same era.
A comprehensive survey of time series forecasting: Concepts, challenges, and future directions, 2025
Mingyue Cheng et al · 2025
Closest in time.
Instructime: Advancing time series classification with multimodal language modeling
Mingyue Cheng et al · 2025
Closest in time.
Cross-domain pre-training with language models for transferable time series representations
Mingyue Cheng, Xiaoyu Tao, Qi Liu, Hao Zhang, Yiheng Chen, and Defu Lian · 2025
Closest in time.
Convtimenet: A deep hierarchical fully convolutional model for multivariate time series analysis
Mingyue Cheng, Jiqian Yang, Tingyue Pan, Qi Liu, Zhi Li, and Shijin Wang · 2025
Closest in time.
Context-alignment: Activating and enhancing llms capabilities in time series
Yuxiao Hu, Qian Li, Dongxiao Zhang, Jinyue Yan, and Yuntian Chen · 2025
Closest in time.
Multi-modal time series analysis: A tutorial and survey
Yushan Jiang, Kanghui Ning, Zijie Pan, Xuyang Shen, Jingchao Ni, Wenchao Yu, Anderson Schneider, Haifeng Chen, Yuriy Nevmyvaka, and Dongjin Song · 2025
Closest in time.
Time-moe: Billion-scale time series foundation models with mixture of experts
Xiaoming Shi, Shiyu Wang, Yuqi Nie, Dianqi Li, Zhou Ye, Qingsong Wen, and Ming Jin · 2025
Closest in time.
Timedart: A diffusion autoregressive transformer for self-supervised time series representation
Daoyu Wang, Mingyue Cheng, Zhiding Liu, and Qi Liu · 2025
Closest in time.
Context is key: A benchmark for forecasting with essential textual information
Andrew Robert Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, and Alexandre Drouin · 2025
Closest in time.