Spatiotemporal Attention Networks for Wind Power Forecasting

Fu, Xingbo; Gao, Feng; Wu, Jiang; Wei, Xinyu; Duan, Fangwei

doi:10.48550/arxiv.1909.07369

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A Deep Neural Network For Spatio-temporal Regression1

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2020

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“…Particularly, they have demonstrated to be a promising approach in capturing the correlations between inputs and outputs while including a natural layer of interpretability to neural models. This attention mechanisms might be introduced at any dimension: spatial [14], temporal [15] or both of them [16].…”

Section: A Deep Neural Network For Spatio-temporal Regressionmentioning

confidence: 99%

A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic Prediction

de Medrano,

Aznarte

2020

Preprint

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Spatio-temporal forecasting is an open research field whose interest is growing exponentially. In this work we focus on creating a complex deep neural framework for spatio-temporal traffic forecasting with comparatively very good performance and that shows to be adaptable over several spatio-temporal conditions while remaining easy to understand and interpret. Our proposal is based on an interpretable attention-based neural network in which several modules are combined in order to capture key spatio-temporal time series components. Through extensive experimentation, we show how the results of our approach are stable and better than those of other state-of-the-art alternatives.

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Section: A Deep Neural Network For Spatio-temporal Regressionmentioning

confidence: 99%