Automated Label Generation for Time Series Classification with Representation Learning: Reduction of Label Cost for Training
This reduces labeling effort for time-series data from end-users and devices, but it is incremental as it builds on existing representation learning techniques.
The paper tackles the problem of high labeling costs for time-series classification by proposing a method to auto-generate labels using few labeled examples, achieving performance close to fully supervised classification on UCR and UCI archives.
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.