Anomaly Detection in Time Series Using GAN

Anomaly detection in time series is critical in the innumerable industry such as cybersecurity, finance, and others. More advanced techniques are required since traditional approaches frequently fail insufficient in capturing shifting anomalies and complex patterns. The study presents a new GANs-based model for detection of time series anomalies. Creating a GAN-based model to efficiently learn normal patterns from time series data is one of the main goals. Another is to compare reconstruction mistakes with current methods to find abnormalities. By comparing reconstruction errors, it is hoped to show how the GAN model may identify subtle and complicated anomalies that may be missed by more conventional techniques such as LSTM and LSTM autoencoders. The NYC Taxi dataset is used to evaluate the effectiveness of the GAN-based method, highlighting its usefulness for anomaly identification in the real world. Reconstruction error analysis alone is not enough to discriminate between normal and anomalous sequences; the approach outperforms existing models by achieving an accuracy of 85%. This comparison demonstrates how GANs may improve anomaly detection systems by identifying subtler abnormalities and more complex patterns.