Fetching the paper…
Reading the bibliography…
Time-series forecasting (TSF) finds broad applications in real-world scenarios.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
N-beats: Neural basis expansion analysis for interpretable time series forecasting
Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. 2019 · 1905
Earlier work this paper cites.
On estimating regression
Elizbar A Nadaraya. 1964 · 1964
Earlier work this paper cites.
Gaussian processes for regression
Christopher Williams and Carl Rasmussen. 1995 · 1995
Earlier work this paper cites.
Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022b · 1995
Earlier work this paper cites.
Application of support vector machines in financial time series forecasting
Francis EH Tay and Lijuan Cao. 2001 · 2001
Earlier work this paper cites.
Time series forecasting using a hybrid arima and neural network model
G Peter Zhang. 2003 · 2003
Earlier work this paper cites.
A review on time series forecasting techniques for building energy consumption
Chirag Deb, Fan Zhang, Junjing Yang, Siew Eang Lee, and Kwok Wei Shah. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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 · 2018
Earlier work this paper cites.
Epideep: Exploiting embeddings for epidemic forecasting
Bijaya Adhikari, Xinfeng Xu, Naren Ramakrishnan, and B Aditya Prakash. 2019 · 2019
Earlier work this paper cites.
Monash time series forecasting archive
Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I Webb, Rob J Hyndman, and Pablo Montero-Manso. 2021 · 2021
Earlier work this paper cites.
Time-series forecasting with deep learning: a survey
Bryan Lim and Stefan Zohren. 2021 · 2021
Cited alongside, same era.
Pretrained transformers as universal computation engines
Kevin Lu, Aditya Grover, Pieter Abbeel, and Igor Mordatch. 2021 · 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 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Darts: User-friendly modern machine learning for time series
Julien Herzen, Francesco Lässig, Samuele Giuliano Piazzetta, Thomas Neuer, Léo Tafti, Guillaume Raille, Tomas Van Pottelbergh, Marek Pasieka, Andrzej Skrodzki, Nicolas Huguenin, et al. 2022 · 2022
Cited alongside, same era.
Azul Garza and Max Mergenthaler-Canseco. 2023 · 2023
Later among the works it cites.
Text-to-audio generation using instruction-tuned llm and latent diffusion model
Deepanway Ghosal, Navonil Majumder, Ambuj Mehrish, and Soujanya Poria. 2023 · 2023
Later among the works it cites.
Large Language Models Are Zero Shot Time Series Forecasters
Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew Gordon Wilson. 2023 · 2023
Later among the works it cites.
Tabllm: Few-shot classification of tabular data with large language models
Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, and David Sontag. 2023 · 2023
Later among the works it cites.
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, et al. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Camul: Calibrated and accurate multi-view time-series forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, and B Aditya Prakash. 2022 · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
A time series is worth 64 words: Long-term forecasting with transformers
Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 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.
Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms
Ching Chang, Wen-Chih Peng, and Tien-Fu Chen. 2023 · 2023
Cited alongside, same era.
A decoder-only foundation model for time-series forecasting
Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou. 2023 · 2023
Cited alongside, same era.
Later among the works it cites.
Large pre-trained time series models for cross-domain time series analysis tasks
Harshavardhan Kamarthi and B Aditya Prakash. 2023 · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
Later among the works it cites.
Promptcast: A new prompt-based learning paradigm for time series forecasting
Hao Xue and Flora D Salim. 2023 · 2023
Later among the works it cites.
Large language models as optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen. 2023 · 2023
Later among the works it cites.
Toward a foundation model for time series data
Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023 · 2023
Later among the works it cites.
One fits all: Power general time series analysis by pretrained lm
Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, and Rong Jin. 2023 · 2023
Later among the works it cites.