What is anomaly detection in time series and how is it implemented in deep learning systems?

Anomaly detection identifies unusual patterns in time series that deviate significantly from expected behavior.

Time series anomaly detection can be statistical (z-score, ARIMA residuals) or deep learning-based (autoencoders, LSTMs). Deep models learn normal temporal patterns and flag deviations with high reconstruction error or prediction error. This is widely used in monitoring systems where labeled anomalies are rare.