# Generating Synthetic Time Series to Augment Sparse Datasets

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/generating-synthetic-time-series-to-augment-sparse-datasets/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Germain Forestier,François Petitjean,Hoang Anh Dau,Geoffrey I. Webb,Eamonn Keogh |
| Citations | 180 |
| DOI | 10.1109/icdm.2017.106 |
| Fields | Computer Science,Decision Sciences |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W2773662615 |
| Type | conference-paper |
| Year | 2017 |

## Paper authors

- [Germain Forestier](https://scholariq.org/researchers/germain-forestier/)
- [François Petitjean](https://scholariq.org/researchers/francois-petitjean/)

## 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/)
- [Data Stream Mining Techniques](https://scholariq.org/topics/data-stream-mining-techniques/)
- [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.