# End-to-end deep representation learning for time series clustering: a comparative study

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/end-to-end-deep-representation-learning-for-time-series-clustering-a-comparative/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Baptiste Lafabrègue,Jonathan Weber,Pierre Gançarski,Germain Forestier |
| Citations | 82 |
| DOI | 10.1007/s10618-021-00796-y |
| Fields | Computer Science,Economics, Econometrics and Finance |
| Open Access | true |
| OA Status | green |
| OA URL | https://hal.science/hal-03407061/document |
| OpenAlex ID | https://openalex.org/W3205581618 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Jonathan Weber](https://scholariq.org/researchers/jonathan-weber/)

## Paper primary topic

- [Time Series Analysis and Forecasting](https://scholariq.org/topics/time-series-analysis-and-forecasting/)

## Paper topics

- [Time Series Analysis and Forecasting](https://scholariq.org/topics/time-series-analysis-and-forecasting/)
- [Anomaly Detection Techniques and Applications](https://scholariq.org/topics/anomaly-detection-techniques-and-applications/)
- [Complex Systems and Time Series Analysis](https://scholariq.org/topics/complex-systems-and-time-series-analysis/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.