Parallel Streaming Implementation Of Online Time Series Correlation Discovery On Sliding Windows With Regression Capabilities
CLOSER: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICES SCIENCE(2019)
摘要
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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关键词
Time Series Correlation and Regression, Data Stream Processing, Distributed Computing
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