Induction of mean output prediction trees from continuous temporal meteorological data

Dima Alberg, Mark Last, Roni Neuman, Avi Sharon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In this paper, we present a novel method for fast data-driven construction of regression trees from temporal datasets including continuous data streams. The proposed Mean Output Prediction Tree (MOPT) algorithm transforms continuous temporal data into two statistical moments according to a user-specified time resolution and builds a regression tree for estimating the prediction interval of the output (dependent) variable. Results on two benchmark data sets show that the MOPT algorithm produces more accurate and easily interpretable prediction models than other state-of-the-art regression tree methods.

Original languageEnglish
Title of host publicationICDM Workshops 2009 - IEEE International Conference on Data Mining
Pages208-213
Number of pages6
DOIs
StatePublished - 1 Dec 2009
Event2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009 - Miami, FL, United States
Duration: 6 Dec 20096 Dec 2009

Publication series

NameICDM Workshops 2009 - IEEE International Conference on Data Mining

Conference

Conference2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009
Country/TerritoryUnited States
CityMiami, FL
Period6/12/096/12/09

Keywords

  • Inductive learning
  • Multivariate statistics
  • Multivariate time series
  • Regression trees
  • Split criteria
  • Temporal prediction
  • Time resolution

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