An Interval Tree Approach to Predict Forest Fires using Meteorological Data
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10.5120/ijca2015907398 |
Dima Alberg. Article: An Interval Tree Approach to Predict Forest Fires using Meteorological Data. International Journal of Computer Applications 132(4):17-22, December 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX
@article{key:article,
author = {Dima Alberg},
title = {Article: An Interval Tree Approach to Predict Forest Fires using Meteorological Data},
journal = {International Journal of Computer Applications},
year = {2015},
volume = {132},
number = {4},
pages = {17-22},
month = {December},
note = {Published by Foundation of Computer Science (FCS), NY, USA}
}
Abstract
Interval prediction can be more useful than single value prediction in many continuous data streams. This paper introduces a novel Interval Prediction Tree IP3 algorithm for interval prediction of numerical target variables from temporal mean-variance aggregated continuous data. This algorithm characterized by: processing incoming mean-variance aggregated multivariate temporal data, splitting each of the continuous features of the input according to the best mean-variance and making stable interval predictions of a target numerical variable with a given degree of statistical confidence. As shown by empirical evaluations in forest fires data set the proposed method provides better performance than existing regression tree models.
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Keywords
Interval Prediction, Mean-Variance Aggregation, Prediction Tree, Forest Fires.