# Germain Forestier

**Type:** Researchers  
**Canonical URL:** https://scholariq.org/researchers/germain-forestier/

## Facts

| Field | Value |
| --- | --- |
| Citations | 8,677 |
| Field | Time Series Analysis and Forecasting |
| h-index | 35 |
| i10-index | 85 |
| Last Known Institution | Université de Haute-Alsace |
| OpenAlex ID | https://openalex.org/A5030958578 |
| ORCID iD | 0000-0002-4960-7554 |
| Works | 217 |

## Researcher papers

- [Deep learning for time series classification: a review](https://scholariq.org/papers/deep-learning-for-time-series-classification-a-review/)
- [Surgical data science for next-generation interventions](https://scholariq.org/papers/surgical-data-science-for-next-generation-interventions/)
- [Surgical data science – from concepts toward clinical translation](https://scholariq.org/papers/surgical-data-science-from-concepts-toward-clinical-translation/)
- [Transfer learning for time series classification](https://scholariq.org/papers/transfer-learning-for-time-series-classification/)
- [Deep learning for colon cancer histopathological images analysis](https://scholariq.org/papers/deep-learning-for-colon-cancer-histopathological-images-analysis/)
- [Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification](https://scholariq.org/papers/dynamic-time-warping-averaging-of-time-series-allows-faster-and-more-accurate/)
- [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://scholariq.org/papers/deep-learning-for-time-series-classification-and-extrinsic-regression-a-current/)
- [Generating Synthetic Time Series to Augment Sparse Datasets](https://scholariq.org/papers/generating-synthetic-time-series-to-augment-sparse-datasets/)
- [Adversarial Attacks on Deep Neural Networks for Time Series Classification](https://scholariq.org/papers/adversarial-attacks-on-deep-neural-networks-for-time-series-classification/)
- [Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm](https://scholariq.org/papers/faster-and-more-accurate-classification-of-time-series-by-exploiting-a-novel/)
- [Evaluating Surgical Skills from Kinematic Data Using Convolutional Neural Networks](https://scholariq.org/papers/evaluating-surgical-skills-from-kinematic-data-using-convolutional-neural/)
- [Surgical motion analysis using discriminative interpretable patterns](https://scholariq.org/papers/surgical-motion-analysis-using-discriminative-interpretable-patterns/)

## Researcher 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/)
- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)
- [Remote-Sensing Image Classification](https://scholariq.org/topics/remote-sensing-image-classification/)
- [Surgical Simulation and Training](https://scholariq.org/topics/surgical-simulation-and-training/)

## Researcher university

- [Université de Haute-Alsace](https://scholariq.org/institutions/universite-de-haute-alsace/)

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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.