Effective Abnormal Activity Detection on Multivariate Time Series
  Healthcare Data
Mengjia Niu Yuchen Zhao Hamed Haddadi
Multivariate time series (MTS) data collected from multiple sensors provide
the potential for accurate abnormal activity detection in smart healthcare
scenarios. However, anomalies exhibit diverse patterns and become unnoticeable
in MTS data. Consequently, achieving accurate anomaly detection is challenging
since we have to capture both temporal dependencies of time series and
inter-relationships among variables. To address this problem, we propose a
Residual-based Anomaly Detection approach, Rs-AD, for effective representation
learning and abnormal activity detection. We evaluate our scheme on a
real-world gait dataset and the experimental results demonstrate an F1 score of
0.839.