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arXiv Machine Learning By Annunziata D'Aversa, Gianvito Pio, Michelangelo Ceci

Modeling spatio-temporal locality in multi-step forecasting of geo-referenced time series

Read the original on arXiv Machine Learning →

The paper introduces SPALT, a method that models spatio‑temporal locality for multi‑step forecasting of geo‑referenced time series. SPALT uses linear model trees to group series with similar trends, injecting spatial features locally, and employs a Reduced Error Pruning strategy that respects spatio‑temporal locality. Experiments on three real‑world renewable‑energy datasets show SPALT outperforms both tree‑based models and state‑of‑the‑art neural networks in forecasting energy production at multiple horizons.

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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