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arXiv Machine Learning By Danyang Li, John Taylor, Thang Bui, Quanling Deng

Season-Aware Hybrid Convolutional-Transformer for Antarctic Sea Ice Concentration Forecasting

Read the original on arXiv Machine Learning →

The paper presents a hybrid Convolutional‑Transformer model for forecasting Antarctic sea ice concentration (SIC) on a monthly basis. It combines convolutional encoding for spatial features with factorised self‑attention to capture spatio‑temporal dependencies, and introduces two seasonal prior mechanisms: a month‑aware positional encoding and a seasonal temporal bias. Experiments show the model outperforms convolutional and recurrent baselines, with ablation studies confirming the benefits of the seasonal priors for both short‑ and long‑horizon predictions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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