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.