Unsupervised LSTMs-based Learning for Anomaly Detection in Highway Traffic Data

Nicola Di Mauro; Stefano Ferilli

doi:10.1007/978-3-030-01851-1_27

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Unsupervised LSTMs-based Learning for Anomaly Detection in Highway Traffic Data

Nicola Di Mauro Stefano Ferilli

Year: 2018 Lecture notes in computer science Pages: 281-290 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-01851-1_27

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Keywords:

Computer science Autoencoder Encoder Deep learning Sequence (biology) Artificial intelligence Anomaly detection Representation (politics) Recurrent neural network Artificial neural network Real-time computing Data mining Machine learning

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1.78

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Topics

Traffic Prediction and Management Techniques

Physical Sciences → Engineering → Building and Construction

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Time Series Analysis and Forecasting

Physical Sciences → Computer Science → Signal Processing

Unsupervised LSTMs-based Learning for Anomaly Detection in Highway Traffic Data

Metrics

Citation History

Topics

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