Spatiotemporal series analysis and forecasting: new deep learning architectures on weather and crime forecasting

Abstract

We investigate spatiotemporal series through weather and crime forecasting and introduce new successful deep learning architectures that are also applicable to other spatiotemporal domains. First, we present our weather model being an alternative to physical models by addressing the computational cost and the performance. In our weather model, we extend the encoder-decoder structure with the Convolutional Long-short Term Memory units and enhance the performance with attention and context matcher mechanisms. We perform experiments on high-scale, real-life, benchmark numerical weather datasets. We show that attention matrices model atmospheric circulations and successfully capture spatial and temporal relations. Our model obtains the best scores among the baseline deep learning models and comparable results with the physical models. Secondly, we study high-resolution crime prediction and introduce a new generative model with Graph Convolutional Gated Recurrent Units (Graph-ConvGRU) and multivariate Gaussian distributions. We introduce a subdivision algorithm and create a graph representation to tackle the sparsity and complexity problem in high-resolution spatiotemporal data. By leveraging the flexible structure of graph representation, we encode the relations to state vectors for each region. We then create a multivariate probability distribution from the state vectors and perform prediction at any resolution for the first time in the literature. Our model obtains the best score in our experiments on real-life and synthetic datasets compared to the state-of-the-art models. Hence our model is not only generative but also precise. We also provide the source code of our algorithms for reproducibility.