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SEE.4C SIGNED

SpatiotEmporal ForEcasting: Coopetition to meet Current Cross-modal Challenges

Total Cost €

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EC-Contrib. €

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Partnership

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 SEE.4C project word cloud

Explore the words cloud of the SEE.4C project. It provides you a very rough idea of what is the project "SEE.4C" about.

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Project "SEE.4C" data sheet

The following table provides information about the project.

Coordinator
FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V. 

Organization address
address: HANSASTRASSE 27C
city: MUNCHEN
postcode: 80686
website: www.fraunhofer.de

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Project website http://www.see4c.eu
 Total cost 760˙806 €
 EC max contribution 760˙806 € (100%)
 Programme 1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT))
 Code Call H2020-ICT-2015
 Funding Scheme CSA
 Starting year 2016
 Duration (year-month-day) from 2016-01-01   to  2018-03-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V. DE (MUNCHEN) coordinator 330˙000.00
2    UNIVERSITAT DE BARCELONA ES (BARCELONA) participant 212˙556.00
3    UNIVERSITE D'AIX MARSEILLE FR (Marseille) participant 201˙125.00
4    RTE RESEAU DE TRANSPORT D'ELECTRICITE FR (PARIS LA DEFENSE CEDEX) participant 17˙125.00

Map

 Project objective

Fast, accurate forecasting of spatiotemporal data is needed in critical industrial domains such as energy (prediction of spatiotemporal patterns in renewable generation, usage and traffic) as well as in public policy. The task is so challenging in scale and scope however as to have been confined mainly to research, while past prize competitions have been limited to forecasts of single dimensional values. Building on our proven success in numerous prize-driven past data challenges, attracting hundreds of participants, we aim to compile and test data grounded on large-scale open European datasets and including specially prepared grid traffic data from Europe’s largest Transportation System Operator. The competition evaluates forecasting algorithms on a cloud platform, tracking accuracy and computational efficiency. Emphasizing cross-specialization knowledge transfer and openness to novel technologies which may spring from different subsectors, we aim to build a platform allowing for coopetitions: the ad-hoc coalescence of competing teams during a challenge aimed at forming sustainable partnerships past the prize scheme itself. We will provide comprehensive documentation for a freely extensible open-source cloud-based specialized computing platform (assembling existing, well tested tools) allowing automated evaluation and feedback as in our latest competitions, but scaled to big data needs. We aim to test this platform and provide baseline results in a smaller scale mini-competition (hackathon). Thus we shall lay the groundwork for a larger prize competition in which evaluation data for predictions may arrive in real or near-real time. We also aim to use our wide contacts with industry, domain and data experts and past participants and winners in order to organize focused meetings of panels to refine value chains in data and algorithms as well as conference workshops, talks and newsletters dedicated to widely advertising challenges to past and new participants.

 Deliverables

List of deliverables.
Data Donor Guidelines Documents, reports 2019-05-30 13:16:02
Hackathon Proceedings Documents, reports 2019-05-30 13:16:00

Take a look to the deliverables list in detail:  detailed list of SEE.4C deliverables.

 Publications

year authors and title journal last update
List of publications.
2016 Florin Popescu, Stephane Ayache, Sergio Escalera, Xavier Baró Solé, Cecile Capponi, Patrick Panciatici, and Isabelle Guyon
From geospatial observations of ocean currents to causalpredictors of spatio-economic activity using computer vision and machine learning
published pages: , ISSN: , DOI:
Geophysical Research Abstracts, vol. 18 2019-06-18

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The information about "SEE.4C" are provided by the European Opendata Portal: CORDIS opendata.

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