Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
On Change Diagnosis in Evolving Data Streams
IEEE Transactions on Knowledge and Data Engineering
A Unifying Framework for Detecting Outliers and Change Points from Time Series
IEEE Transactions on Knowledge and Data Engineering
Online Mass Flow Prediction in CFB Boilers
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Handling outliers and concept drift in online mass flow prediction in CFB boilers
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers
DS '09 Proceedings of the 12th International Conference on Discovery Science
Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift
ACM SIGKDD Explorations Newsletter
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In this paper we consider the problem of online detection of gradual and abrupt changes in sensor data having high levels of noise and outliers. We propose a simple heuristic method based on the Quantile Index (QI) and study how robust this method is for detecting both gradual and abrupt changes with such data. We evaluate the performance of our method on the artificially generated and real datasets that represent different operational settings of a pilot circulating fluidized bed (CFB) reactor and CFB cold model. Our experiments suggest that QI can be used for designing very simple yet effective methods for gradual change detection in the noisy sensor data. It can be also used for detecting abrupt changes in the data unless they occur too often one after another.