SpatioTemporal GRU for Trajectory Classification Spatiotemporal trajectory classification is a fundamental problem for locationbased services with many realworld applications such as travel mode classification, animal mobility detection, and location recommendation. In the literature, many approaches have been proposed to solve this classification task including deep learning models like LSTM recently for sequence classification. However, these approaches fail to consider both spatial and temporal interval information simultaneously, but share some common drawbacks: omitting either the spatial information or the temporal interval information out. Some models like TimeLSTM, have been proposed to handle the temporal interval information for spatiotemporal trajectories, but they do not take into account the spatial information. Note that, considering both spatial and temporal interval information is crucial for spatiotemporal data mining in order not to miss any spatiotemporal pattern. In this study, we propose a trajectory classifier called SpatioTemporal GRU to better model the spatiotemporal correlations and irregular temporal intervals prevalently present in spatiotemporal trajectories. We introduce a novel segmented convolutional weight mechanism to capture shortterm local spatial correlations in trajectories and propose an additional temporal gate to control the information flow related to the temporal interval information. Performance evaluation demonstrates that our proposed model outperforms popular deep learning approaches for the travel model classification problem.

Unsupervised Anomaly Detection in Multivariate SpatioTemporal Datasets Using Deep Learning. Techniques used for spatiotemporal anomaly detection in an unsupervised settings has attracted great attention in recent years. It has extensive use in a wide variety of applications such as: medical diagnosis, sensor events analysis, earth science, fraud detection systems, etc. Most of the real world time series datasets have spatial dimension as additional context such as geographic location. Although many temporal data are spatiotemporal in nature, existing techniques are limited to handle both contextual (spatial and temporal) attributes during anomaly detection process. Taking into account of spatial context in addition to temporal context would help uncovering complex anomaly types and unexpected and interesting knowledge about problem domain. In this paper, a new approach to the problem of unsupervised anomaly detection in a multivariate spatiotemporal dataset is proposed using a hybrid deep learning framework. The proposed approach is composed of a Long Short Term Memory (LSTM) Encoder and Deep Neural Network (DNN) based classifier to extract spatial and temporal contexts. Although the approach has been employed on crime dataset from San Francisco Police Department to detect spatiotemporal anomalies, it can be applied to any spatiotemporal datasets.