A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
Self-supervised learning for time series analysis: Taxonomy, progress, and prospects
Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
One fits all: Power general time series analysis by pretrained lm
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 …
processing (NLP) and computer vision (CV), limited progress has been made for general …
Transformers in time series: A survey
Transformers have achieved superior performances in many tasks in natural language
processing and computer vision, which also triggered great interest in the time series …
processing and computer vision, which also triggered great interest in the time series …
Film: Frequency improved legendre memory model for long-term time series forecasting
Recent studies have shown that deep learning models such as RNNs and Transformers
have brought significant performance gains for long-term forecasting of time series because …
have brought significant performance gains for long-term forecasting of time series because …
Large models for time series and spatio-temporal data: A survey and outlook
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 …
applications. They capture dynamic system measurements and are produced in vast …
TFAD: A decomposition time series anomaly detection architecture with time-frequency analysis
Time series anomaly detection is a challenging problem due to the complex temporal
dependencies and the limited label data. Although some algorithms including both …
dependencies and the limited label data. Although some algorithms including both …
Dcdetector: Dual attention contrastive representation learning for time series anomaly detection
Time series anomaly detection is critical for a wide range of applications. It aims to identify
deviant samples from the normal sample distribution in time series. The most fundamental …
deviant samples from the normal sample distribution in time series. The most fundamental …
Weakly guided adaptation for robust time series forecasting
Robust multivariate time series forecasting is crucial in many cyberphysical and Internet of
Things applications. Existing state-of-the-art robust forecasting models decompose time …
Things applications. Existing state-of-the-art robust forecasting models decompose time …
Foundation models for time series analysis: A tutorial and survey
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 …
cornerstone for extracting valuable insights crucial to a myriad of real-world applications …