  IoT time series analysis has found numerous applications in a wide variety of
areas, ranging from health informatics to network security. Nevertheless, the
complex spatial temporal dynamics and high dimensionality of IoT time series
make the analysis increasingly challenging. In recent years, the powerful
feature extraction and representation learning capabilities of deep learning
(DL) have provided an effective means for IoT time series analysis. However,
few existing surveys on time series have systematically discussed unsupervised
DL-based methods. To fill this void, we investigate unsupervised deep learning
for IoT time series, i.e., unsupervised anomaly detection and clustering, under
a unified framework. We also discuss the application scenarios, public
datasets, existing challenges, and future research directions in this area.
