# Data augmentation using synthetic data for time series classification with deep residual networks

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/data-augmentation-using-synthetic-data-for-time-series-classification-with-deep/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Hassan Ismail Fawaz,Germain Forestier,Jonathan Weber,Lhassane Idoumghar,Pierre-Alain Müller |
| Citations | 73 |
| DOI | 10.48550/arxiv.1808.02455 |
| Fields | Computer Science,Decision Sciences |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/1808.02455 |
| OpenAlex ID | https://openalex.org/W2886572283 |
| Type | preprint |
| Year | 2018 |

## Paper authors

- [Hassan Ismail Fawaz](https://scholariq.org/researchers/hassan-ismail-fawaz/)
- [Lhassane Idoumghar](https://scholariq.org/researchers/lhassane-idoumghar/)

## Paper journal

- [arXiv (Cornell University)](https://scholariq.org/journals/arxiv-cornell-university/)

## 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/)
- [Stock Market Forecasting Methods](https://scholariq.org/topics/stock-market-forecasting-methods/)

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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.