## 2 Related Work  
The task of detecting outliers or anomalous events in data has been studied extensively in the context of time series and spatial data separately. Outlier detection studies for time-series data find outliers considering only the temporal context [14; 15]. For data with spatial context, several context-based anomaly detection techniques have been proposed [16; 17; 18; 19; 20]. Spatio-temporal outlier detection methods are significantly more challenging because of the additional difficulty of jointly modeling the spatial and temporal continuity in the dataset [2; 3]. [PERSON] and [PERSON] [5] propose a spatio-temporal outlier detection algorithm using a modified version of DBSCAN. It is a neighborhood-based approach which first defines spatial outliers based on spatial neighborhood, then checks the temporal context of spatial outliers using their temporal neighbors. However, their algorithm does not generate a score for data points. [PERSON] and [PERSON] [2] propose a four-step sequential approach to identify spatio-temporal outliers using classification, aggregation, comparison, and verification. [PERSON] et al. [20] introduce the notion of context-aware anomaly detection by integrating the system logs and time series measurement data in distributed systems. They propose a two-stage clustering methodology using a principle component analysis (PCA) [21] based method and K-Means [22] algorithm to extract context and metric patterns. The methods mentioned above have something in common: They do not use the spatial and temporal components jointly to detect anomalies. They first find spatial outliers using spatial context. Then, they use temporal context to define temporal neighborhood of spatial outliers to detect if they are temporal outliers too. They all use either a modified version of DBSCAN [7], which is a spatial clustering-based outlier detection algorithm, or LOF [9], which is a locality-based outlier detection algorithm, to find neighborhoods. The main challenge with distance-based methods is that they are computationally too expensive for large multivariate datasets. The idea of isolation and IsolationForest algorithm have been successfully applied in anomaly detection and rare class classification problems in different data domains. [PERSON] et al. [23] apply IsolationForest analysis into the landcover image dataset to measure the representativeness of training areas in the image. [PERSON] et al. [24] propose an isolation-based online anomalous trajectory detection system to detect abnormal or malicious events using trajectories obtained from GlobalPosition System (GPS)-enabled taxis. [PERSON] et al. [25] propose an IsolationForest based conditional anomaly detection approach for fraud detection. Besides traditional distance- and isolation-based methods, deep learning-based anomaly detection approaches have recently gained a lot of attention. LSTM networks have been used for anomaly detection on time series data as it achieves better generalization capability than traditional methods. A LSTM network-based Encoder-Decoder scheme for anomaly detection was recently proposed where the model learns to reconstruct 'normal' time series data and uses reconstruction error to detect anomalies [26]. A recent deep learning anomaly detection approach combining a convolutional neural network (CNN), LSTM, and deep neural network (DNN) were designed to detect traffic anomalies in web servers [27], where they used fully labeled dataset for supervised learning and solved a univariate time series classification problem. Another deep learning framework combining a convolutional encoder, attention-based convolutional LSTM network, and convolutional decoder for detecting spatio-temporal anomalies for multivariate time series data was proposed [28]. In addition to network complexity, their approach requires the calculation of multiscale system signature and residual signature matrices to perform anomaly detection thereby increasing the overall complexity. [PERSON] et al.