Anomaly Detection using PCA in Time Series Data

Anomaly detection in time series data is a crucial task with applications spanning various industries. Uncovering unusual patterns within temporal datasets can lead to insights and early identification of critical events. Principal Component Analysis (PCA) has emerged as an effective technique for anomaly detection in time series data. By reducing the dimensionality of the data while retaining its essential variability, PCA enables the identification of deviations from expected patterns. This abstract provides an overview of anomaly detection using PCA in time series data, exploring its theoretical foundation, practical benefits, challenges, and real-world implementations. The integration of PCA in anomaly detection empowers businesses and organizations to enhance decision-making processes and mitigate risks through timely anomaly identification.