Time series: theory and methods
Time series: theory and methods
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
Interpreting Stale Load Information
IEEE Transactions on Parallel and Distributed Systems
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Multivariate Data Reduction and Discrimination with SAS Software
Multivariate Data Reduction and Discrimination with SAS Software
Dependency Detection in MobiMine and Random Matrices
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Anomaly detection of web-based attacks
Proceedings of the 10th ACM conference on Computer and communications security
Structural analysis of network traffic flows
Proceedings of the joint international conference on Measurement and modeling of computer systems
A pragmatic approach to dealing with high-variability in network measurements
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Workload-Aware Load Balancing for Clustered Web Servers
IEEE Transactions on Parallel and Distributed Systems
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Optimal multi-scale patterns in time series streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Local Correlation Tracking in Time Series
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Models and framework for supporting runtime decisions in Web-based systems
ACM Transactions on the Web (TWEB)
ACM Computing Surveys (CSUR)
Clustering of time series data-a survey
Pattern Recognition
Projection approximation subspace tracking
IEEE Transactions on Signal Processing
Analysis and control of correlated web server queues
Computer Communications
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Efficient system management requires continuous knowledge about the state of system and application resources that are typically represented through time series obtained by monitors. Capacity planning studies, forecasting, state aggregation, anomaly and event detection would be facilitated by evidence of data correlations. Unfortunately, the high variability characterizing most monitored time series affects the accuracy and robustness of existing correlation solutions. This paper proposes an innovative approach that is especially tailored to detect linear and non-linear correlation between time series characterized by high variability. We compare the proposed solution and existing algorithms in terms of accuracy and robustness for several synthetic and real settings characterized by low and high variability, linear and non-linear correlation. The results show that our proposal guarantees analogous performance for low variable time series, and improves state of the art in finding correlations in highly variable domains that are of interest for the application context.