Using the R-MAPE index as a resistant measure of forecast accuracy.

Background: The mean absolute percentage error (MAPE) is probably the most widely used goodness-of-fit measure. However, it does not meet the validity criterion due to the fact that the distribution of the absolute percentage errors is usually skewed to the right, with the presence of outlier values. In these cases, MAPE overstates the corresponding population parameter.

Study of the factors associated with substance use in adolescence using Association Rules.

The aim of this study is to analyse the factors related to the use of addictive substances in adolescence using association rules, descriptive tools included in Data Mining. Thus, we have a database referring to the consumption of addictive substances in adolescence, and use the free distribution program in the R arules package (version 2.10.

The methodology of Data Mining. An application to alcohol consumption in teenagers.

This paper is aimed mainly at making researchers in the field of drug addictions aware of a methodology of data analysis aimed at knowledge discovery in databases (KDD). KDD is a process consisting of a series of phases, the most characteristic of which is called data mining (DM), whereby different modelling techniques are applied in order to detect patterns and relationships among the data. Common and differentiating factors between the most widely used DM techniques are analysed, mainly from a methodological viewpoint, and their use is exemplified using data related to alcohol consumption in teenagers and its possible relationship with personality variables (N=7030).