Conventional time series models are restricted to narrow historical data patterns, missing out on product metadata, ...

A research team led by Prof. Li Hai from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has developed a novel deep learning framework that significantly improves the ...

Multivariate time series anomaly detection is crucial in sensitive domains such as cybersecurity and grid monitoring, significantly contributing to the reliability and safety of system operation.

Future studies may further optimize the scale decomposition and fusion modules. Such efforts could enhance the representation of multi-scale information and help address key challenges in multivariate ...

For multivariate time series classification, current research predominantly focuses on contrastive learning to acquire suitable representations. Despite their successes in enhancing accuracy and ...

The research demonstrates how time series methods reveal the patterns of emotion fluctuation at both individual and group levels. The study also discusses the application of multivariate time series ...

Multivariate Time Series Pipeline A demonstration of building a tractable, feature engineering pipeline for multivariate time series. Read more in the article Building a Tractable, Feature Engineering ...

Time series data refers to a sequence of data points collected or recorded at regular time intervals. This type of data is prevalent across various domains, such as economics, weather, health, and ...