U-TSS: a novel time series segmentation model based U-net applied to automatic detection of interference events in geomagnetic field data.

ABSTRACT: With the rapid advancement of Internet of Things (IoT) technology, the volume of sensor data collection has increased significantly. These data are typically presented in the form of time series, gradually becoming a crucial component of big data. Traditional time series analysis methods struggle with complex patterns and long-term dependencies, whereas deep learning technologies offer new solutions. This study introduces the U-TSS, a U-net-based sequence-to-sequence fully convolutional network, specifically designed for one-dimensional time series segmentation tasks. U-TSS maps input sequences of arbitrary length to corresponding sequences of class labels across different temporal scales. This is achieved by implicitly classifying each individual time point in the input time series and th

SUBMITTER: Shan W 

PROVIDER: S-EPMC11888873 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature