Summarization of Spacecraft Telemetry Data by Extracting Significant Temporal Patterns

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.

[1]  Eleazar Eskin,et al.  Anomaly Detection over Noisy Data using Learned Probability Distributions , 2000, ICML.

[2]  Lloyd Allison,et al.  Change-Point Estimation Using New Minimum Message Length Approximations , 2002, PRICAI.

[3]  Eamonn J. Keogh,et al.  A Probabilistic Approach to Fast Pattern Matching in Time Series Databases , 1997, KDD.

[4]  Clu-istos Foutsos,et al.  Fast subsequence matching in time-series databases , 1994, SIGMOD '94.

[5]  Mitsuru Ishizuka,et al.  PRICAI 2002: Trends in Artificial Intelligence , 2002, Lecture Notes in Computer Science.

[6]  Jaideep Srivastava,et al.  Event detection from time series data , 1999, KDD '99.

[7]  J. V. van Wijk,et al.  Cluster and calendar based visualization of time series data , 1999, Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis'99).

[8]  Xindong Wu,et al.  Research and Development in Knowledge Discovery and Data Mining , 1998, Lecture Notes in Computer Science.

[9]  Paul R. Cohen,et al.  Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Series , 2001, Sequence Learning.

[10]  C. S. Wallace,et al.  Minimum Message Length Segmentation , 1998, PAKDD.

[11]  Eamonn J. Keogh,et al.  Finding surprising patterns in a time series database in linear time and space , 2002, KDD.

[12]  Heikki Mannila,et al.  Rule Discovery from Time Series , 1998, KDD.

[13]  Cyrus Shahabi,et al.  TSA-tree: a wavelet-based approach to improve the efficiency of multi-level surprise and trend queries on time-series data , 2000, Proceedings. 12th International Conference on Scientific and Statistica Database Management.