An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
The Journal of Machine Learning Research
High-quantile modeling for customer wallet estimation and other applications
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Data Mining with R: Learning with Case Studies
Data Mining with R: Learning with Case Studies
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Research on time series forecasting is mostly focused on point predictions - models are obtained to estimate the expected value of the target variable for a certain point in future. However, for several relevant applications this type of forecasts has limited utility (e.g. costumer wallet value estimation, wind and electricity power production, control of water quality, etc.). For these domains it is frequently more important to be able to forecast a range of plausible future values of the target variable. A typical example is wind power production, where it is of high relevance to predict the future wind variability in order to ensure that supply and demand are balanced. This type of predictions will allow timely actions to be taken in order to cope with the expected values of the target variable on a certain future time horizon. In this paper we study this type of predictions - the prediction of a range of expected values for a future time interval. We describe some possible approaches to this task and propose an alternative procedure that our extensive experiments on both artificial and real world domains show to have clear advantages.