Foundation models for time series analysis: A tutorial and survey

Y Liang, H Wen, Y Nie, Y Jiang, M Jin, D Song… - Proceedings of the 30th …, 2024 - dl.acm.org
Time series analysis stands as a focal point within the data mining community, serving as a
cornerstone for extracting valuable insights crucial to a myriad of real-world applications …

[HTML][HTML] Data augmentation techniques in time series domain: a survey and taxonomy

G Iglesias, E Talavera, Á González-Prieto… - Neural Computing and …, 2023 - Springer
With the latest advances in deep learning-based generative models, it has not taken long to
take advantage of their remarkable performance in the area of time series. Deep neural …

One fits all: Power general time series analysis by pretrained lm

T Zhou, P Niu, L Sun, R Jin - Advances in neural …, 2023 - proceedings.neurips.cc
Although we have witnessed great success of pre-trained models in natural language
processing (NLP) and computer vision (CV), limited progress has been made for general …

Transformers in time series: A survey

Q Wen, T Zhou, C Zhang, W Chen, Z Ma, J Yan… - arXiv preprint arXiv …, 2022 - arxiv.org
Transformers have achieved superior performances in many tasks in natural language
processing and computer vision, which also triggered great interest in the time series …

Time-llm: Time series forecasting by reprogramming large language models

M Jin, S Wang, L Ma, Z Chu, JY Zhang, X Shi… - arXiv preprint arXiv …, 2023 - arxiv.org
Time series forecasting holds significant importance in many real-world dynamic systems
and has been extensively studied. Unlike natural language process (NLP) and computer …

Deep learning for time series classification and extrinsic regression: A current survey

N Mohammadi Foumani, L Miller, CW Tan… - ACM Computing …, 2024 - dl.acm.org
Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

Hyporadise: An open baseline for generative speech recognition with large language models

C Chen, Y Hu, CHH Yang… - Advances in …, 2024 - proceedings.neurips.cc
Advancements in deep neural networks have allowed automatic speech recognition (ASR)
systems to attain human parity on several publicly available clean speech datasets …

Large models for time series and spatio-temporal data: A survey and outlook

M Jin, Q Wen, Y Liang, C Zhang, S Xue, X Wang… - arXiv preprint arXiv …, 2023 - arxiv.org
Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

A survey on time-series pre-trained models

Q Ma, Z Liu, Z Zheng, Z Huang, S Zhu… - … on Knowledge and …, 2024 - ieeexplore.ieee.org
Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …

Watermarking for out-of-distribution detection

Q Wang, F Liu, Y Zhang, J Zhang… - Advances in …, 2022 - proceedings.neurips.cc
Abstract Out-of-distribution (OOD) detection aims to identify OOD data based on
representations extracted from well-trained deep models. However, existing methods largely …