###### Abstract
* Despite the effectiveness of IsolationForest technique, it isolates data points based on one random feature at a time which may not represent spatial and temporal dependencies between data points properly in the spatio-temporal dataset. * The definition of distance between data points in multivariate spatio-temporal data with mixed attributes is often challenging. This difficulty may have an adverse effect on outlier detection performance of distance-based clustering algorithms. To address these challenges, we propose a hybrid deep autoencoder framework. It is composed of a multi-channel convolutional neural network (CNN) and Long Short-Term Memory (LSTM) network, named as MC-CNN-LSTM. The architecture of the proposed hybrid framework is based on the autoencoder scheme which has the greater power in representing arbitrary data distributions for anomaly detection than commonly used alternatives such as PCA or matrix factorization [12]. The multi-channel CNN network is deployed on the encoder side for learning spatial structures of each spatio-temporal multivariate data frame, and LSTM network is deployed on the decoder side for learning temporal patterns from the encoded spatial structures. It is designed to solve the unsupervised anomaly detection problem in non-image multivariate spatio-temporal data. Our major contributions in this paper can be summarized as follows:  
* In the aforementioned neighborhood methods, the biggest challenge is to combine the contextual attributes in a meaningful way. In the proposed approach, spatial and temporal contexts are handled by different deep learning components as these contextual variables refer to different types of dependencies. * All distance-/clustering-based algorithms are proximity-based algorithms and dependent on some notion of distance. Many distance functions used for proximity quantification (such as Euclidean and Manhattan) are not meaningful for all datatypes. * The proposed hybrid framework is designed to be trained in a truly unsupervised fashion without any labels indicating normal or abnormal data. The architecture is robust enough to learn the underlying dynamics even if the training dataset contains noise and anomalies. * In the proposed hybrid architecture, CNN and LSTM layers are combined into one unified framework that is jointly trained. Unlike proximity-based methods, the framework can be easily implemented on data with mixed-type attributes and scales well to multivariate datasets with high number of features. We compared the proposed model with the state-of-the-art spatial and spatio-temporal anomaly detection algorithms: ST-Outlier detection algorithm [5], ST-BDBCAN [6], LDBCSCAN [8], LOF [9], and to a powerful model-based anomaly detection method, IsolationForest [10]. We conducted extensive experiments using the year 2005 Gulf of Mexico (West) buoy dataset from National Data Buoy Center ([[https://www.ndbc.noaa.gov/](https://www.ndbc.noaa.gov/)]([https://www.ndbc.noaa.gov/](https://www.ndbc.noaa.gov/))) and Hurricane Katrina best track path data [13] as ground truth for the experiments. Our results demonstrate that the proposed deep learning framework achieves superior results by achieving at least 10% improvement in accuracy over the methods used in comparison. The remainder of this paper is organized as follows. Section 2 provides an overview of related work in the literature. Section 3 gives theoretical background on autoencoders, autoencoder based anomaly detection, CNN and LSTM networks. Section 4 explains methodology, spatio-temporal data pre-processing and the proposed hybrid deep learning framework. Section 5 gives detailed information on experimental setup, Hurricane Katrina dataset and results of the experiments. Section 6 discusses the results, summarizes contributions and gives the future research directions.