Time series: theory and methods
Time series: theory and methods
Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Discovering Piecewise Linear Models of Grid Workload
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Towards Non-Stationary Grid Models
Journal of Grid Computing
Dynamic Model Selection With its Applications to Novelty Detection
IEEE Transactions on Information Theory
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This paper deals with a statistical model fitting procedure for non-stationary time series. This procedure selects the parameters of a piecewise autoregressive model using the Minimum Description Length principle. The existing chromosome representation of the piecewise autoregressive model and its corresponding optimisation algorithm are improved. First, we show that our proposed chromosome representation better captures the intrinsic properties of the piecewise autoregressive model. Second, we apply an optimisation algorithm, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), with which our setup converges faster to the optimal fit. Our proposed method achieves at least one order of magnitude performance improvement compared to the existing solution.