Browsing by Subject "Spatiotemporal modeling"

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Open Access
Modeling non-stationary dynamics of spatio-temporal sequences with self-organizing point process models
(2021-06) Karaahmetoğlu, Oğuzhan
We investigate the challenging problem of modeling the non-stationary dynam-ics of spatio-temporal sequences for prediction applications. Spatio-temporal se-quence modeling has critical real-life applications such as natural disaster, social, and criminal event prediction. Even though this problem has been thoroughly studied, many approaches do not address the non-stationarity and sparsity of the spatio-temporal sequences, which are frequently observed in real-life sequences. Here, we introduce a novel prediction algorithm that is capable of modeling non-stationarity in both time and space. Moreover, our algorithm can model both densely and sparsely populated sequences. We partition the spatial region with a decision tree, where each node of the tree corresponds to a subregion. We model the event occurrences in di˙erent subregions in space with individual but inter-acting point processes. Our algorithm can jointly optimize the partitioning tree and the interacting point processes through a gradient-based optimization. We compare our approach with statistical models, probabilistic approaches, and deep learning based approaches, and show that our model achieves the best forecasting performance on real-life datasets such as earthquake and criminal event records.
Open Access
Spatio-temporal forecasting over graphs with deep learning
(2020-12) Ceyani, Emir
We study spatiotemporal forecasting of high-dimensional rectangular grid graph structured data, which exhibits both complex spatial and temporal dependencies. In most high-dimensional spatiotemporal forecasting scenarios, deep learningbased methods are widely used. However, deep learning algorithms are overconfident in their predictions, and this overconfidence causes problems in the human-in-the-loop domains such as medical diagnosis and many applications of 5 th generation wireless networks. We propose spatiotemporal extensions to variational autoencoders for regularization, robustness against out-of data distribution, and incorporating uncertainty in predictions to resolve overconfident predictions. However, variational inference methods are prone to biased posterior approximations due to using explicit exponential family densities and mean-field assumption in their posterior factorizations. To mitigate these problems, we utilize variational inference & learning with semi-implicit distributions and apply this inference scheme into convolutional long-short term memory networks(ConvLSTM) for the first time in the literature. In chapter 3, we propose variational autoencoders with convolutional long-short term memory networks, called VarConvLSTM. In chapter 4, we improve our algorithm via semi-implicit & doubly semi-implicit variational inference to model multi-modalities in the data distribution . In chapter 5, we demonstrate that proposed algorithms are applicable for spatiotemporal forecasting tasks, including space-time mobile traffic forecasting over Turkcell base station networks.