## 5 Experiments
### Algorithms Used for Comparison  
To validate the effectiveness of our approach, we have examined spatio-temporal outliers detected by our model and by the following state-of-the art anomaly detection techniques: ST-Outlier detection algorithm [5], ST-BDSCAN [6], LDBSCAN [8], LOF [9], and Isolation Forest [10]. We have implemented ST-Outlier detection and LDBSCAN algorithms in Python 3.6.8 programming language. For LOF ([[https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html)]([https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html))) and IsolationForest ([[https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html)]([https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html))) methods, we used implementations available in the scikit-learn ([[https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)]([https://scikit-learn.org/stable/](https://scikit-learn.org/stable/))), which is a free machine learning library for Python programming language. We have used Euclidean distance for all proximity-based clustering algorithms since it has generated better results compared to other distance metrics. As ST-Outlier detection algorithm does not return a score, it is not included in the quantitative evaluation tests.  
Model parameters were selected through randomized search over a predefined list for each parameter. For LDBSCAN algorithm, following values were assigned to the parameters since they gave the best result, as stated in [6]: LOFUB = 4, MinPtsLOF = 180, MinPtsLDBSCAN = 225, and \(pct=0.27\). For the LOF algorithm, we used 50 for number of neighbors to use for k-nearest neighbor calculations, and 0.1 for contamination ratio. For the ST-Outlier detection algorithm, the following values were assigned to the parameters: _Eps1_ = 100 (distance parameter for spatial attribute), _Eps2_ = 0.2 (distance parameter for non-spatial attribute), _MinPts_ = 5, which is the minimum number of points within _Eps1_ and _Eps2_ distance of a point. For IsolationForest, we have used 100 base estimators (number of trees), and contamination was set to 0.1.  
Besides the above baseline methods, we also evaluate the depth of the components of the framework for the sample dataset: Number of layers in MC-CNN-Encoder component and number of layers in LSTM-decoder component. The quantitative evaluation of variants of the framework is given in the Section 5.5.3.  
In order to compare our approach quantitatively to models which return an outlierness score, we have used three different OAHII (Outliers Association with Hurricane Intensity Index) measures developed by [PERSON] et al. [6]. OAHII measures show how much the generated scores for each observation are correlated with the impact of the ground truth spatio-temporal anomaly which is the Hurricane Katrina for the test data. While OAHIIII measures the Pearson correlation between the outlierness score and actual impact of the hurricane on each data point and impact, OAHII2 measures the recall power of models using the impact vector as weight factor in weighted correlation, a derivative of Pearson correlation, and OAHII3 measures the precision power of models using the scores vector as weight vector in weighted correlation.