SpatiotEmporal ForEcasting: Coopetition to meet Current Cross-modal Challenges

Informazioni relative al progetto

SEE.4C

ID dell’accordo di sovvenzione: 688356

DOI

10.3030/688356

Progetto chiuso

Data della firma CE 28 Ottobre 2015

Data di avvio 1 Gennaio 2016

Data di completamento 31 Marzo 2018

Finanziato da

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

Costo totale

€ 760 806,25

Contributo UE

€ 760 806,25

Coordinato da

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)

Periodo di rendicontazione: 2017-07-01 al 2018-03-31

Sintesi del contesto e degli obiettivi generali del progetto

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.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

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.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

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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