SpatiotEmporal ForEcasting: Coopetition to meet Current Cross-modal Challenges

Información del proyecto

SEE.4C

Identificador del acuerdo de subvención: 688356

DOI

10.3030/688356

Proyecto cerrado

Fecha de la firma de la CE 28 Octubre 2015

Fecha de inicio 1 Enero 2016

Fecha de finalización 31 Marzo 2018

Financiado con arreglo a

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

Coste total

€ 760 806,25

Aportación de la UE

€ 760 806,25

Coordinado por

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)

Período documentado: 2017-07-01 hasta 2018-03-31

Resumen del contexto y de los objetivos generales del proyecto

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.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

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.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

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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