@inproceedings{dec08a4527154832b0810b4a489d0fea,
title = "Induction of mean output prediction trees from continuous temporal meteorological data",
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.",
keywords = "Inductive learning, Multivariate statistics, Multivariate time series, Regression trees, Split criteria, Temporal prediction, Time resolution",
author = "Dima Alberg and Mark Last and Roni Neuman and Avi Sharon",
year = "2009",
month = dec,
day = "1",
doi = "10.1109/ICDMW.2009.30",
language = "English",
isbn = "9780769539027",
series = "ICDM Workshops 2009 - IEEE International Conference on Data Mining",
pages = "208--213",
booktitle = "ICDM Workshops 2009 - IEEE International Conference on Data Mining",
note = "2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009 ; Conference date: 06-12-2009 Through 06-12-2009",
}