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

arXiv: https://arxiv.org/abs/2107.05458 · HTML: https://mlsanity.com/paper/2107.05458/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Multivariate LSTM-FCNs for Time Series Classification](https://mlsanity.com/paper/1801.04503/) (2018)
- [Hierarchical Clustering using Auto-encoded Compact Representation for Time-series Analysis](https://mlsanity.com/paper/2101.03742/) (2021)

## Built on by (most cited first, 1 of 1)

- [An Active Learning Framework with a Class Balancing Strategy for Time Series Classification](https://mlsanity.com/paper/2405.12122/) (2024)

## Abstract (arXiv)

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.