SpatiotEmporal ForEcasting: Coopetition to meet Current Cross-modal Challenges

Informations projet

SEE.4C

N° de convention de subvention: 688356

DOI

10.3030/688356

Projet clôturé

Date de signature de la CE 28 Octobre 2015

Date de début 1 Janvier 2016

Date de fin 31 Mars 2018

Financé au titre de

• INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)

Coût total

€ 760 806,25

Contribution de l’UE

€ 760 806,25

Coordonné par

FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV
Germany

Periodic Reporting for period 2 - SEE.4C (SpatiotEmporal ForEcasting: Coopetition to meet Current Cross-modal Challenges)

Période du rapport: 2017-07-01 au 2018-03-31

Résumé du contexte et des objectifs généraux du projet

The objective of this project was to design a means of evaluating the performance of big data analytics algorithms in the field of spatio-temporal forecasting. This is a relevant and important problem in various fields such as smart grid management, renewable energy use optimization, media, climate science, economics, finance, and has recently been empowered in big data access, computing power and algorithmic-oriented research. The objective of our work was to harness these developments, especially in the use of open-source analytics in allowing the evaluation of state-of-the-art spatio-temporal forecasting algorithms on a test problem of industrial and social relevance.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

We have assembled relevant test data sets in energy and media, put together evaluation rules for algorithms which include speed and accuracy of prediction, implemented a web-based submission evaluation platform along with tools and rules for formatting of algorithms for the scope of their evaluation, assembled a Linux-container evaluation sandbox capable of executing several open-source languages and hundred of analytics toolboxes including support for GPU-powered state-of-the-art deep learning algorithms.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

The competition design allows for automatic evaluation of larger data and more powerful algorithms than the current state-of-the-art in forecasting challenges. The potential impact is the fostering of a new direction in big data analytics and data science in a prize competition accessible to individual data scientists, research laboratories and companies alike.

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