Parallel Streaming Implementation of Online Time Series Correlation Discovery on Sliding Windows with Regression Capabilities
Résumé
This paper addresses the problem of continuously finding highly correlated pairs of time series over the most recent time window and possibly use the discovered correlations to select features for training a regression model for prediction. The implementation builds upon the ParCorr parallel method for online correlation discovery and is designed to run continuously on top of the UPM-CEP data streaming engine through efficient streaming operators.
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