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4. Anomaly Detection In Mixed Time-Series Using A Convolutional Sparse Representation With Application To Spacecraft Health Monitoring

Anomaly Detection In Mixed Time-Series Using A Convolutional Sparse Representation With Application To Spacecraft Health Monitoring

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IEEE ICASSP 2020 Virtual Conference May 2020

This paper introduces a convolutional sparse model for anomaly detection in mixed continuous and discrete data. This model, referred to as C-ADDICT, builds upon the experiences of our previous ADDICT algorithm. It can handle discrete and continuous data j

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Anomaly Detection In Mixed Time-Series Using A Convolutional Sparse Representation With Application To Spacecraft Health Monitoring

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This paper introduces a convolutional sparse model for anomaly detection in mixed continuous and discrete data. This model, referred to as C-ADDICT, builds upon the experiences of our previous ADDICT algorithm. It can handle discrete and continuous data j