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Solution for Error and Attenuation Detection in Network Services Based on Time Series Decomposition Model: Pattern Mining Based on Time Series Decomposition via REFII Model and Data Science Methods Usage
Abstract
This chapter introduces an innovative methodology for error and attenuation detection in network services by unifying the REFII time series model with Bayesian networks (BNs), and complementary data science techniques traditionally applied outside temporal analytics. The proposed framework addresses the critical challenge of identifying rare, high-impact events—such as signal degradation in mobile or fixed networks—that compromise service quality but are often obscured by complex temporal dependencies and imbalanced data distributions. This transformation facilitates non-temporal expansion, a novel interpolation process that enriches time series with contextual events and operational parameters lacking inherent temporal markers. Proposed solution unites advanced analytical techniques with decision making process for pattern mining like Bayesian networks, decision trees, FTP, K-Means clustering.
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