[Apologies for cross-postings]
################################################################## CALL FOR CHALLENGE PARTICIPATION MoReBikeS: Model Reuse with Bike rental Station data Discovery Challenge #1 of ECML PKDD 2015, Porto, Portugal, September 7-11, 2015 http://reframe-d2k.org/Challenge ################################################################## === CALL FOR CHALLENGE PARTICIPATION === Adaptive reuse of learnt knowledge is of critical importance in the majority of knowledge-intensive application areas, particularly when the context in which the learnt model operates can be expected to vary from training to deployment. This challenge therefore focuses on model reuse and context change. The challenge is carried out in the framework of historical bicycle rental data obtained from Valencia, Spain. Bicycles are continuously taken from and returned to rental stations across the city. The data consists of time series describing hourly availability of bikes at each station; information on weather and (local) holidays is also provided. The challenge motivation is based on the fact that, while we may have had the opportunity to learn and tune good models for old stations with historical data, we do not always have the same amount of data for new stations. With that in mind, participants will receive, in addition to limited data for the new stations, a large number of trained models for old stations. The task will be to make predictions (3 hours ahead) with regard to the number of bikes available for these new stations and within the next months. This situation fluctuates considerably depending on the time of year, the station's location, etc. The key point here is that by using models from other stations that have been learnt from data spanning more than one year, better predictions can be made for the new stations. In the end, this challenge aims at promoting the reusability of models rather than retraining a different model again and again each time the context changes. The challenge ends with the challenge workshop at ECML PKDD 2015 on September 11, 2015 in Porto, Portugal. The challenge workshop is organised jointly with the LMCE 2015 workshop on 'The value of model reuse', see http://users.dsic.upv.es/~flip/LMCE2015 === TASK === The task is to predict the number of bikes in the stations 3 hours in advance. === PARTICIPATION === This challenge is open for everyone to participate by submitting predictions to the public leaderboard which is refreshed on May 4, 18, 25 and June 1. The results of the last leaderboard will be immediately published as the final results of the small test set challenge. We encourage everyone to participate in the full test set challenge as well. For this it is required to submit the code and a paper describing the chosen prediction method by June 8 and the predictions on full test data by June 22. The main focus of the paper should be to explain the solution to other participants and interested people, comparison to other existing methods is not required. The accepted papers are presented at the challenge workshop at ECML PKDD 2015 on September 11, 2015. The winner of the MoReBikeS challenge is the presenting author with lowest mean absolute error predictions on the full test data. === IMPORTANT DATES === * March 31, 2015: Training and deployment data, models, and leaderboard test data on-line * May 4, 18, 25 and June 1, 2015: Leaderboard refreshed for submissions up to that date * June 8, 2015: Deadline to submit paper and source code * June 9, 2015: Full test data available * June 22, 2015: Deadline to submit predictions on the full test set * July 6, 2015: Notification of acceptance * August 3, 2015: Deadline to submit camera-ready version * September 11, 2015: Challenge workshop at ECML PKDD 2015, final results announced === PRIZE === Three participants who provided the best predictions on the full test set will be offered to have full ECML-PKDD 2015 conference fee paid for (at the early rate). === MORE DETAILS === See more details at http://reframe-d2k.org/Challenge === ORGANISING COMMITTEE === Nicolas Lachiche, University of Strasbourg, France Meelis Kull, University of Bristol, UK Adolfo Martínez-Usó, Universitat Politècnica de Valencia, Spain
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