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Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.
SOTA for Traffic Prediction on METR-LA
Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation.
To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by recurrent neural network (RNN) with attention mechanism.
First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp.
In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases.