Summarization of spacecraft telemetry data by extracting significant temporal patterns

Abstract

This paper presents a method to summarize massive spacecraft telemetry data by extracting significant event and change patterns in the low-level time-series data. This method first transforms the numerical time-series into a symbol sequence by a clustering technique using DTW distance measure, then detects event patterns and change points in the sequence. We demonstrate that our method can successfully summarize the large telemetry data of an actual artificial satellite, and help human operators to understand the overall system behavior.