Anomaly Detection from Kepler Satellite Time-Series Data

Abstract

Kepler satellite data is analyzed to detect anomalies within the short cadence light curve using traditional statistical algorithms and neural networks. Windowed mean division normalization is presented as a method to transform non-linear data to linear data. Modified Z-score, general extreme studentized deviate, and percentile rank algorithms were applied to initially detect anomalies. A refined windowed modified Z-score algorithm was used to determine “true anomalies” that were then used to train both a Pattern Neural Network and Recurrent Neural Network to detect anomalies. For speed in detection, trained neural networks have the clear advantage. However, the additional tuning and complexity of training means that unless speed is the primary concern traditional statistical methods are easier to use and equally effective at detection.