Multidimensional Prediction Models When the Resolution Context Changes

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-23525-7_31Multidimensional data is systematically analysed at multiple granularities by applying aggregate and disaggregate operators (e.g., by the use of OLAP tools). For instance, in a supermarket we may want to predict sales of tomatoes for next week, but we may also be interested in predicting sales for all vegetables (higher up in the product hierarchy) for next Friday (lower down in the time dimension). While the domain and data are the same, the operating context is different. We explore several approaches for multidimensional data when predictions have to be made at different levels (or contexts) of aggregation. One method relies on the same resolution, another approach aggregates predictions bottom-up, a third approach disaggregates predictions top-down and a final technique corrects predictions using the relation between levels. We show how these strategies behave when the resolution context changes, using several machine learning techniques in four application domains.This work was supported by the Spanish MINECO under grants TIN 2010-21062-C02-02 and TIN 2013-45732-C4-1-P, and the REFRAME project, granted by the European Coordinated Research on Longterm Challenges in Information and Communication Sciences Technologies ERA-Net (CHIST-ERA), and funded by MINECO in Spain (PCIN-2013-037) and by Generalitat Valenciana PROMETEOII2015/013.Martínez Usó, A.; Hernández Orallo, J. (2015). Multidimensional Prediction Models When the Resolution Context Changes. En Machine Learning and Knowledge Discovery in Databases. Springer. 509-524. https://doi.org/10.1007/978-3-319-23525-7_31S509524Agrawal, R., Gupta, A., Sarawagi, S.: Modeling multidimensional databases. In: Proceedings of the Thirteenth International Conference on Data Engineering, ICDE 1997, pp. 232–243. 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