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An Interval Tree Approach to Predict Forest Fires using Meteorological Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 132 - Number 4 |
| Year of Publication: 2015 |
| Authors: Dima Alberg |
10.5120/ijca2015907398
|
Dima Alberg . An Interval Tree Approach to Predict Forest Fires using Meteorological Data. International Journal of Computer Applications. 132, 4 ( December 2015), 17-22. DOI=10.5120/ijca2015907398
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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