SpatiotEmporal ForEcasting: Coopetition to meet Current Cross-modal Challenges

Informacje na temat projektu

SEE.4C

Identyfikator umowy o grant: 688356

DOI

10.3030/688356

Projekt został zamknięty

Data podpisania przez KE 28 Października 2015

Data rozpoczęcia 1 Stycznia 2016

Data zakończenia 31 Marca 2018

Finansowanie w ramach

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

Koszt całkowity

€ 760 806,25

Wkład UE

€ 760 806,25

Koordynowany przez

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)

Okres sprawozdawczy: 2017-07-01 do 2018-03-31

Podsumowanie kontekstu i ogólnych celów projektu

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.

Prace wykonane od początku projektu do końca okresu sprawozdawczego oraz najważniejsze dotychczasowe rezultaty

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.

Innowacyjność oraz oczekiwany potencjalny wpływ (w tym dotychczasowe znaczenie społeczno-gospodarcze i szersze implikacje społeczne projektu)

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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