Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Learning to Recognize Time Series: Combining ARMA models with memory-based learning
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Variational Learning for Switching State-Space Models
Neural Computation
Time series clustering and classification by the autoregressive metric
Computational Statistics & Data Analysis
Adaptive threshold computation for CUSUM-type procedures in change detection and isolation problems
Computational Statistics & Data Analysis
Computation of the ARL for CUSUM-S2 schemes
Computational Statistics & Data Analysis
Aircraft engine health monitoring using self-organizing maps
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Aircraft engine fleet monitoring using self-organizing maps and edit distance
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
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Regime switching models, in which the state of the world is locally stationary, are a useful abstraction for many continuous valued data streams. In this paper we develop an online framework for the challenging problem of jointly predicting and annotating streaming data as it arrives. The framework consists of three sequential modules: prediction, change detection and regime annotation, each of which may be instantiated in a number of ways. We describe a specific realisation of this framework with the prediction module implemented using recursive least squares, and change detection implemented using CUSUM techniques. The annotation step involves associating a label with each regime, implemented here using a confidence interval approach. Experiments with simulated data show that this methodology can provide an annotation that is consistent with ground truth. Finally, the method is illustrated with foreign exchange data.