Forecasting Problem:

Forecast a dataset of given time series from distinct domains as accurately as possible, using methods from the computational intelligence area and applying a consistent methodology. The data consists of time series with different time frequencies, that include yearly, quarterly and monthly data.

Methods:

The prediction competition is open to all methods of computational intelligence, incl. fuzzy method, artificial neural networks, evolutionary algorithms, decision & regression tress, support vector machines, hybrid approaches etc. used in all areas of forecasting, prediction & time series analysis, etc. Ensemble techniques are also allowed, if they employ any CI method. The contestants will use a unique consistent methodology for all time series.

Evaluation:

The only evaluation criterion is the Symmetric Mean Absolute Percentage Error (SMAPE): where F_t is t-th forecasted value, A_t is t-th actual (real) value, and n is the forecast horizon. The results will be uncovered during the IFSA-EUSFLAT 2015 at the end of the Special Session on Computational Intelligence in Forecasting organized under the auspices of the EUSFLAT Working Group on Learning and Data Mining.

Competition Participation:

Organizers as well as their department/institute colleagues are exempted form the competition evaluation but they are welcome to submit their results that will serve as comparison benchmarks. Their paper submission are also welcome.

Conference Participation:

While we do not require this, we strongly encourage the participants to register for IFSA-EUSFLAT 2015 and to submit a contribution to the related special session.

Prize:

The prize for the winner is 100EUR cash. Participation on the CIF special session of the IFSA-EUSFLAT 2015 conference is a strict condition to obtain the prize.



We look forward to your submission and also to meet you in Gijón on the IFSA-EUSFLAT 2015 conference.


Kind regards & good luck!
Martin Štěpnička, Michal Burda, Paulo Cortez and Juan Peralta Donate.

We proudly acknowledge the endorsement of CIF under the wings of EUSFLAT Working Group on Learning and Data Mining.