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

Automated Label Generation for Time Series Classification with Representation Learning: Reduction of Label Cost for Training