An Analysis of Time-Series Techniques for Oracle Database’s Anomaly Detection
Keywords:
time-series analysis, anomaly detection, Oracle databases, ARIMAAbstract
Time-series analysis has emerged as a primary instrument for detecting anomalies in Oracle database systems. These strategies facilitate the identification of performance degradation, security breaches, or data inaccuracies by highlighting consistent trends. This study aims to examine a range of sophisticated time-series methodologies, including Autoregressive Integrated Moving Average (ARIMA), Seasonal Decomposition of Time Series (STL), Long Short-Term Memory (LSTM) networks, and hybrid statistical-machine learning models. These methods are specifically designed to enhance anomaly detection in structured database systems, hence bolstering system security and reliability.
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