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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 …
A survey on time-series pre-trained models
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 …
practical applications. Deep learning models that rely on massive labeled data have been …
Transformers in time series: A survey
Generative pretrained hierarchical transformer for time series forecasting
Recent efforts have been dedicated to enhancing time series forecasting accuracy by
introducing advanced network architectures and self-supervised pretraining strategies …
introducing advanced network architectures and self-supervised pretraining strategies …
Timemixer: Decomposable multiscale mixing for time series forecasting
Time series forecasting is widely used in extensive applications, such as traffic planning and
weather forecasting. However, real-world time series usually present intricate temporal …
weather forecasting. However, real-world time series usually present intricate temporal …
Hdmixer: Hierarchical dependency with extendable patch for multivariate time series forecasting
Multivariate time series (MTS) prediction has been widely adopted in various scenarios.
Recently, some methods have employed patching to enhance local semantics and improve …
Recently, some methods have employed patching to enhance local semantics and improve …
A multi-scale decomposition mlp-mixer for time series analysis
Time series data, including univariate and multivariate ones, are characterized by unique
composition and complex multi-scale temporal variations. They often require special …
composition and complex multi-scale temporal variations. They often require special …