Time-Series Anomaly Detection

Built a reconstruction-error autoencoder for multivariate time-series anomaly detection on industrial IoT sensor data. Sliding window inputs, LSTM encoder/decoder, trained on 6 months of normal operation. Achieved a precision of 0.91 and recall of 0.88 on labeled anomaly events. Deployed to AWS Lambda with a DynamoDB alerting backend.