With the numerous amount of applications and demand for data analysis, a full outlier detection mechanism focused on longitudinal data is built with the aim of providing knowledge regarding data's underlying nature.
After training a DBN, each instance is scored using a sliding window approach. Score distributions are then scrutinized providing a clear boundary for anomaly classification.
SAX discretization and dimensionality reduction is available for continuos data prior to the modeling phase.
A flexible tree-augmented DBN modeling procedure with easily adapted parameters.
After performing anomaly detection, output data sets can be downloaded free of charge!
MultivariatE Time sEries OutlieR (METEOR) is a complete anomaly detection system ranging from pre-processing to post-score analysis using dynamic Bayesian networks (DBN)
Data is processed ensuring the system's assumptions including data visualization
Optimal tree-augmented DBN modeling through a straightforward user interface
DBN structures can be downloaded and changed at any time through the whole procedure
According to the modeled DBN, data set windows are scored and analyzed
Complete Multivariate Time Series are scored and evaluated
Score analysis strategies are studied and easily adapted through the user interface