  Time-series generated by end-users, edge devices, and different wearables are
mostly unlabelled. We propose a method to auto-generate labels of un-labelled
time-series, exploiting very few representative labelled time-series. Our
method is based on representation learning using Auto Encoded Compact Sequence
(AECS) with a choice of best distance measure. It performs self-correction in
iterations, by learning latent structure, as well as synthetically boosting
representative time-series using Variational-Auto-Encoder (VAE) to improve the
quality of labels. We have experimented with UCR and UCI archives, public
real-world univariate, multivariate time-series taken from different
application domains. Experimental results demonstrate that the proposed method
is very close to the performance achieved by fully supervised classification.
The proposed method not only produces close to benchmark results but
outperforms the benchmark performance in some cases.
