Details

Autor(en) / Beteiligte

• Lin, Shuyu
• Clark, Ronald
• Birke, Robert
• Schonborn, Sandro
• Trigoni, Niki
• Roberts, Stephen

Titel

Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model

Ist Teil von

• ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, p.4322-4326

Ort / Verlag

IEEE

Erscheinungsjahr

2020

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• In this work, we propose a VAE-LSTM hybrid model as an unsupervised approach for anomaly detection in time series. Our model utilizes both a VAE module for forming robust local features over short windows and a LSTM module for estimating the long term correlation in the series on top of the features inferred from the VAE module. As a result, our detection algorithm is capable of identifying anomalies that span over multiple time scales. We demonstrate the effectiveness of our detection algorithm on five real world problems and find our method outperforms three other commonly used detection methods.