(Fanaee-T & Gama, 2014) Dataset

Context:
- It can support a (Fanaee-T & Gama, 2014) Bike Sharing Count Prediction Task.
See: Bike Sharing Service, Sensor Network.

References

(Fanaee-T & Gama, 2014) ⇒ Hadi Fanaee-T, and Joao Gama. "Event Labeling Combining Ensemble Detectors and Background Knowledge.” In: Progress in Artificial Intelligence, 2. doi:10.1007/s13748-013-0040-3
- QUOTE: Event labeling is the process of marking events in unlabeled data. Traditionally, this is done by involving one or more human experts through an expensive and time-consuming task. In this article we propose an event labeling system relying on an ensemble of detectors and background knowledge. The target data are the usage log of a real bike sharing system. We first label events in the data and then evaluate the performance of the ensemble and individual detectors on the labeled data set using ROC analysis and static evaluation metrics in the absence and presence of background knowledge. Our results show that when there is no access to human experts, the proposed approach can be an effective alternative for labeling events. In addition to the main proposal, we conduct a comparative study regarding the various predictive models performance, semi-supervised and unsupervised approaches, train data scale, time series filtering methods, online and offline predictive models, and distance functions in measuring time series similarity

http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset
- Bike sharing systems are new generation of traditional bike rentals wherein the whole process, from membership, rental, to returning the bike back, has become automatic. Through these systems, the user is able to easily rent a bike from a particular position and return it at a different location. Currently, there are about over 500 bike-sharing programs around the world consisting of over 500 thousands bicycles. Today, there exists great interest in these systems due to the important role they have in minimizing traffic, environmental and health issues.
  Apart from the mentioned benefits we get from bike sharing systems, the characteristics of data being generated by these systems also make them attractive for research. Opposed to other transport services such as bus or subway, the duration of travel, and departure and arrival positions are explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important events in the city could be detected via monitoring these data.