INPRET: The Interval Prediction Tree algorithm for temporal numerical data

Dima Alberg, Mark Last

Research output: Contribution to journalArticlepeer-review


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.

Original languageAmerican English
Pages (from-to)407-418
Number of pages12
JournalIntelligent Decision Technologies
Issue number4
StatePublished - 1 Jan 2016


  • Interval prediction
  • data streams
  • mean-variance aggregation
  • outliers detection
  • prediction tree
  • time resolution

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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