###### Abstract  
Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most settings, spatial context is often expressed in terms of ZIP code or region coordinates such as latitude and longitude. However, traditional anomaly detection techniques cannot handle more than one contextual attribute in a unified way. In this paper, a new hybrid approach based on deep learning is proposed to solve the anomaly detection problem in multivariate spatio-temporal dataset. It works under the assumption that no prior knowledge about the dataset and anomalies are available. The architecture of the proposed hybrid framework is based on an autoencoder scheme, and it is more efficient in extracting features from the spatio-temporal multivariate datasets compared to the traditional spatio-temporal anomaly detection techniques. We conducted extensive experiments using buoy data of 2005 from National Data Buoy Center and Hurricane Katrina as ground truth. Experiments demonstrate that the proposed model achieves more than 10% improvement in accuracy over the methods used in the comparison where our model jointly processes the spatial and temporal dimensions of the contextual data to extract features for anomaly detection. spatio-temporal anomaly detection; unsupervised learning; multivariate data; deep learning; CNN; LSTM; hurricane tracking; Hurricane Katrina +
Footnote †: journal: Medical  
For a problem domain, obtaining the labelled training data for all types of anomalies is often too expensive if not impossible [4]. This highlights the need for unsupervised techniques to find spatio-temporal anomalies. Moreover, spatio-temporal datasets are generally multivariate and have many contextual structures in them (spatial and temporal regularities) which makes them particularly difficult for labelling and well suited for unsupervised learning models. In the unsupervised scenarios, the type of anomalies and the ratio of anomalous events within the given dataset are generally not known. In such scenarios, we need to model the normal behavior of the underlying system in the presence of anomalies which pose extra difficulty. In the past few years, some density- and clustering-based unsupervised spatio-temporal anomaly detection algorithms have been developed. The most prominent ones are ST-Outlier detection algorithm [5] and ST-BDBCAN [6] methods which are basically an extension of DBSCAN [7], a density-based spatial clustering algorithm. LDBSCAN [8], a locality-based outlier detection algorithm, is the merge of both DBSCAN and Local Outlier Factor (LOF) [9]. IsolationForest [10; 11] is a powerful approach for anomaly detection in multivariate data without relying on any distance or density measure. In particular, it is an unsupervised, tree-based ensemble method that applies the novel concept of isolation to anomaly detection. It detects anomalies based on a fundamentally different model-based approach: an anomalous point is isolated via recursive partitioning by randomly selecting a feature and then randomly selecting a split value for the selected feature. The output is the anomaly score for each data point. Although it establishes a powerful multivariate non-parametric approach, it works on continuous-valued data only. Despite the inherent unsupervised setting, they may still not detect anomalies effectively due to the following reasons:  
* In multivariate time series data, strong temporal dependency exists between time steps. Due to this reason, distance-/clustering-based methods, may not perform well since they cannot capture temporal dependencies properly across different time steps. * On the other hand, for the case of high-dimensional data, even the data locality may be ill-defined because of data sparsity [3].