Deep Convolutional Clustering based Time-series Anomaly Detection
- Performed exploratory data analysis of real world machine sensor data
- Performed feature selection to identify the KPIs
- Performed feature engineering of time series data to convert raw data into sequences
- Developed a novel anomaly detection algorithm to detect anomalies
- Applied K-Means clustering to the embedded space of a convolutional autoencoder and optimized the network jointly