Time-Series Analysis Techniques for Anomaly Detection in Oracle Databases
Abstract
Time series analysis is turn out to be a crucial technique for anomaly detection in Oracle databases. This allows the identification of irregular patterns that will indicate performance degradation, security breaches, or data inconsistencies. The objective of this research paper is to explore the advanced series methodologies which includes autoregressive integrated moving average (ARIMA), seasonal decomposition of time series (STL), long short-term memory (LSTM) networks, and hybrid statistical-machine learning models tailored for detecting anomalies in structured database environments.
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References
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