Abstract
In this paper, the time-series model is used to predict whether an ocean buoy is about to be inside a vortex. Marine buoys are an important tool for collecting ocean data and studying ocean dynamics, climate change, and ecosystem health. A vortex is an important ocean dynamic process. If we can predict that a buoy is about to enter a vortex, we can automatically adjust the buoy’s sampling frequency to better observe the vortex’s structure and development. To address this requirement, based on the profile data, including latitude and longitude, temperature, and salinity, collected by 56 buoys in the Arctic Ocean from 2014 to 2023, this paper uses the TSMixer time-series model to predict whether an ocean buoy is about to be inside a vortex. The TSMixer model effectively captures the spatio-temporal characteristics of multivariate time series through time-mixing and feature-mixing mechanisms, and the accuracy of the model reaches 84.6%. The proposed model is computationally efficient and has a low memory footprint, which is suitable for real-time applications and provides accurate prediction support for marine monitoring.