Natural spatiotemporal processes can be highly non-stationary in many ways, eg the low-
level non-stationarity such as spatial correlations or temporal dependencies of local pixel …

Spatiotemporal predictive learning aims to generate future frames by learning from historical
frames. In this paper, we investigate existing methods and present a general framework of …

We present PredRNN++, a recurrent network for spatiotemporal predictive learning. In
pursuit of a great modeling capability for short-term video dynamics, we make our network …

The predictive learning of spatiotemporal sequences aims to generate future images by
learning from the historical context, where the visual dynamics are believed to have modular …

Recent years have witnessed remarkable advances in spatiotemporal predictive learning,
incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training …

We present STanHop-Net (Sparse Tandem Hopfield Network) for multivariate time series
prediction with memory-enhanced capabilities. At the heart of our approach is STanHop, a …

Effective long-term predictions have been increasingly demanded in urban-wise data mining
systems. Many practical applications, such as accident prevention and resource pre …

Forecasting complex time series is ubiquitous and vital in a range of applications but
challenging. Recent advances endeavor to achieve progress by incorporating various deep …

As industrial systems become more complex and monitoring sensors for everything from
surveillance to our health become more ubiquitous, multivariate time series prediction is …

Time-series forecasting is an important problem across a wide range of domains. Designing
accurate and prompt forecasting algorithms is a non-trivial task, as temporal data that arise …