Induction of Mean Output Prediction Trees from Continuous Temporal Meteorological Data
Temporal prediction, inductive learning, time resolution, regression trees, split criteria, multivariate statistics, multivariate time series
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.