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Parallel Streaming Implementation of Online Time Series Correlation Discovery on Sliding Windows with Regression Capabilities
Acte de colloque   Open Access

Parallel Streaming Implementation of Online Time Series Correlation Discovery on Sliding Windows with Regression Capabilities

Boyan Kolev, Reza Akbarinia, Ricardo Jimenez-Peris, Oleksandra Levchenko, Florent Masseglia, Marta Patino et Patrick Valduriez
Proceedings of the 9th International Conference on Cloud Computing and Services Science, Vol.Volume 1: ADITCA, pp.681-687
CLOSER 2019 - 9th International Conference on Cloud Computing and Services Science (Heraklion, Greece, 02/05/2019–04/05/2019)
2019
DOI: https://doi.org/10.5220/0007843806810687

Résumé

Data Stream Processing Time Series Correlation and Regression Distributed Computing
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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