Affiliations: [a] Department of Industrial Engineering and Management, SCE - Shamoon College of Engineering, Beer-Sheva, Israel | [b] Department of Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Correspondence:
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Corresponding author: Dima Alberg, Department of Industrial Engineering and Management, SCE - Shamoon College of Engineering, Beer-Sheva, Israel. Tel.: +972 8 647 5649; Fax: +972 8 647 5643; E-mail:dimitria@sce.ac.il
Abstract: This paper presents Interval Prediction
Tree INPRET algorithm for interval prediction of numerical
target variables from temporal mean-variance aggregated data. The proposed
algorithm allows to process mean-variance aggregated multivariate temporal
data and to identify outliers in training data instances. The proposed
algorithm enables, on the one hand, to utilize predictive feature
information obtained from mean and variance of temporally aggregated
instances, and on the other hand, to achieve a considerable reduction in the
depth of the induced prediction tree by using interval prediction tree
leaves. As shown by our empirical evaluation of aircraft maintenance real
world multi-sensor data set, in terms of the prediction tree size and the
root mean square error, the proposed algorithm provides better integration
between accuracy and performance than existing regression tree models.
Keywords: Interval prediction, outliers detection, mean-variance aggregation, time resolution, prediction tree, data streams
