## 2 Background
### Bike Demand Prediction Models  
In the literature, we can currently find numerous works on station demand prediction. Some address the problem from a data mining point of view [15] with an exploratory data approach [16]. Afterwards, works like [PERSON] et al. [17] show how to implement prediction models based on time series analysis methods like Auto-Regressive Moving Average (ARMA).  
Subsequently, in May 2014, Kaggle [18], a known machine learning competition platform, launched a competition about the Washington D.C. BSS. In this competition, participants were asked to combine historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare [19] program in Washington, D.C. Due to this competition, a lot of researchers began to work in prediction models to obtain the forecast of bike demand in stations.  
[PERSON] et al. [20] use the data described above to in the analysis of several regression algorithms from state of the art. They applied these algorithms to predict the global use of the bike sharing system. They establish baseline predictors as references of performance and apply Ridge Regression, Adaboost Regression, Support Vector Regression, Random Forest Regression and Gradient Tree Boosting Regression. They perform predictions up to 24 h ahead and their study covers the whole system instead of each station independently. [PERSON] et al. [21] also present prediction models about the total number of bikes rented per hours in the system in their work. The authors compare Random Forest, Conditional inference trees and Gradient Boosted Machines. [PERSON] [22] perform a great comparison of the approaches proposed by other authors, while others like [PERSON] et al. [23], [PERSON] et al. [24], [PERSON] et al. [25] among others in their dissertation. This work offers a great overview of past research works on this topic.  
Previous works are interesting if we want to look at total demand in the entire bike sharing system but nowadays it is possible to obtain station data from Open Data services [26; 27] and private services [28] which can be used to build prediction models for each station and forecast the demand. This will allow to provide useful information for a further bike reallocation strategy and avoid shortages.  
[PERSON] et al. [29] present an interesting work where they achieve the previous two objectives: prediction models for each station and a rebalancing algorithm. The data employed in this work provided information about Chicago BSS [30] and Washington [31] bike systems and it was related to the trips travelled in the system. In the system proposal section of this work it will be explained what kind of information is contained in trip data. The authors propose the use of Gradient BoostingMachines, Poisson Regression and Random Forest to build prediction models and they use a variant of the Vehicle Routing Problem (VRP) with the framework Jsprint to perform the reallocation strategy. The authors do not provide any open source code nor an in-depth explanation.  
In this work, we will employ some well-known methods which have also been used in the described literature, to predict the demand at each station and provide useful information for the subsequent reallocation task, which will be studied in a future work.