Dealing with temporal and spatial correlations to classify outliers in geophysical data streams

Anomaly detection and change analysis are challenging tasks in stream data mining. We illustrate a novel method that addresses both these tasks in geophysical applications. The method is designed for numeric data routinely sampled through a sensor network. It extends the traditional time series forecasting theory by accounting for the spatial information of geophysical data. In particular, a forecasting model is computed incrementally by accounting for the temporal correlation of data which exhibit a spatial correlation in the recent past. For each sensor the observed value is compared to its spatial-aware forecast, in order to identify the outliers. Finally, the spatial correlation of outliers is analyzed, in order to classify changes and reduce the number of false anomalies. The performance of the presented method is evaluated in both artificial and real data streams.