- 6
- 4
- 2
- 1
- 1
Thomas Guyet
14
Documents
Identifiants chercheurs
- thomas-guyet
- 0000-0002-4909-5843
- Google Scholar : https://scholar.google.com/citations?user=cuNKrFoAAAAJ
- IdRef : 171949307
Présentation
Research interests
------------------
My research area is artificial intelligence (AI) with a multidisciplinary approach - algorithmic, design methodologies and cognitive science. I am particularly interested in discovering spatial and temporal patterns in semantically rich datasets. My areas of application are related to agronomy (mainly landscapes) and health (care pathways analysis).
My first research direction is the **temporal and spatial pattern mining**. Data from the observation of living systems (agricultural and medical systems) have a strong spatial or temporal dimension. But the spatial and temporal information is often underutilized in the data mining algorithms. The challenge lies in identifying new kind of temporal/spatial patterns that have valuable properties to make possible their extraction by complete and correct algorithms. A recent approach I'm developping is the use declarative programming, more especially **[Answer Set Programming (ASP)](https://www.cs.utexas.edu/users/vl/papers/wiasp.pdf)** with [clingo](https://potassco.org), to mix pattern mining and reasonning.
My second research direction aims at better including the user in the loop of knowledge discovery. Specifically, I am interested in implementing **interactive systems to support users in their exploration process** of large datasets. To acheive this goal I propose an enactive point of view of the data interpretation that brings creative solutions to take into account the cognitive ergonomy of the knowledge discovery tools.
Publications
- 4
- 3
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 14
- 5
- 4
- 4
- 3
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 1
- 1
- 1
- 1
- 1
- 1
- 1
Extraction des zones cohérentes par l’analyse spatio-temporelle d’images de télédétectionRevue Internationale de Géomatique, 2015, Numéro spécial SAGEO 2014, pp.22. ⟨10.3166/rig.25.473-494⟩
Article dans une revue
hal-01184095v1
|
|
|
Clustering Flood Events from Water Quality Time-Series using Latent Dirichlet Allocation ModelWater Resources Research, 2013, 49 (12), pp.8187-8199. ⟨10.1002/2013WR014086⟩
Article dans une revue
halshs-00906292v1
|
|
Prédiction du niveau de nappes phréatiques : comparaison d'approches locale, globale et hybrideEGC 2022 - Conférence francophone sur l'Extraction et la Gestion des Connaissances, Jan 2022, Blois, France
Communication dans un congrès
hal-03548071v1
|
|
Désagrégation temporelle du cumul annuel de croissance de l'herbeEGC 2022 - Conférence francophone sur l'Extraction et gestion des connaissances, Jan 2022, Blois, France. pp.27-38
Communication dans un congrès
hal-03548073v1
|
|
Temporal Disaggregation of the Cumulative Grass GrowthICPRAI 2022 - 3rd International Conference on Pattern Recognition and Artificial Intelligence, Jun 2022, Paris, France. pp.383-394, ⟨10.1007/978-3-031-09282-4_32⟩
Communication dans un congrès
hal-03899264v1
|
|
Probabilistic forecasting of seasonal time series Combining clustering and classification for forecastingITISE 2021 - 7th International Conference on Time Series and Forecasting, Jul 2021, Gran Canaria, Spain. pp.1-13
Communication dans un congrès
hal-03326626v1
|
|
Toward a Framework for Seasonal Time Series Forecasting Using ClusteringIDEAL 2019, Nov 2019, Manchester, United Kingdom. pp.328-340, ⟨10.1007/978-3-030-33607-3_36⟩
Communication dans un congrès
hal-02371221v1
|
|
Day-ahead time series forecasting: application to capacity planningAALTD'18 - 3nd ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, Sep 2018, Dublin, Ireland
Communication dans un congrès
hal-01912002v1
|
|
PerForecast : un outil de prévision de l'évolution de séries temporelles pour le planning capacitaireEGC 2018 - Conférence Extraction et Gestion des Connaissances, Jan 2018, Paris, France. pp.455-458
Communication dans un congrès
hal-01911243v1
|
|
Bag-of-Temporal-SIFT-Words for Time Series ClassificationECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, Sep 2015, Porto, Portugal
Communication dans un congrès
halshs-01184900v1
|
|
Extraction des zones cohérentes par l'analyse spatio-temporelle d'images de télédétectionProceedings of the Spatial Analysis and Geography conference (SAGEO), Nov 2014, Grenoble, France. ⟨10.3166/HSP.2014.1-14⟩
Communication dans un congrès
hal-01088643v1
|
1d-SAX : une nouvelle représentation symbolique pour les séries temporellesConférence Extraction et Gestion de Connaissances, Jan 2014, Rennes, France
Communication dans un congrès
hal-00916970v1
|
|
|
1d-SAX: A Novel Symbolic Representation for Time SeriesInternational Symposium on Intelligent Data Analysis, 2013, United Kingdom. pp.273-284, ⟨10.1007/978-3-642-41398-8_24⟩
Communication dans un congrès
halshs-00912512v1
|
|
Dense Bag-of-Temporal-SIFT-Words for Time Series ClassificationAdvanced Analysis and Learning on Temporal Data, Springer, 2016, 978-3319444116. ⟨10.1007/978-3-319-44412-3_2⟩
Chapitre d'ouvrage
hal-01252726v4
|