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arXiv AI By Yaohua Zha, Hai Wu, Shu-Tao Xia

DST-Net: Decomposed Spatio-Temporal Network for Time Series Forecasting

Read the original on arXiv AI →

The paper introduces DST‑Net, a decomposed spatio‑temporal network that separately models trend and seasonal components of time series data. It employs a Latent Graph Block for the trend branch to capture global spatial dependencies and a Gaussian Multi‑scale Attention mechanism for the seasonal branch to focus on local temporal patterns while reducing attention distraction. Experiments on real‑world benchmarks show that this tailored approach improves forecasting performance.

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 AI.

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