Time Series Classification (TSC) has been an important and challenging task in data mining. Transfer learning has been widely applied in computer vision and natural language processing applications to improve deep neurological network's generalization capabilities.
Time Series Classification (TSC) has been an important and challenging task
in data mining, especially on multivariate time series and multi-view time
series data sets. Meanwhile, transfer learning has been widely applied in
computer vision and natural language processing applications to improve deep
neural network's generalization capabilities. However, very few previous works
applied transfer learning framework to time series mining problems.
Particularly, the technique of measuring similarities between source domain and
target domain based on dynamic representation such as density estimation with
importance sampling has never been combined with transfer learning framework.
In this paper, we first proposed a general adaptive transfer learning framework
for multi-view time series data, which shows strong ability in storing
inter-view importance value in the process of knowledge transfer. Next, we
represented inter-view importance through some time series similarity
measurements and approximated the posterior distribution in latent space for
the importance sampling via density estimation techniques. We then computed the
matrix norm of sampled importance value, which controls the degree of knowledge
transfer in pre-training process. We further evaluated our work, applied it to
many other time series classification tasks, and observed that our architecture
maintained desirable generalization ability. Finally, we concluded that our
framework could be adapted with deep learning techniques to receive significant
model performance improvements.