Experimental evaluation of Time-Series Gradient Boosting Tree with Time-Series Benchmark datasets

Abstract

In this paper, We evaluated the time-series gradient boosting decision tree method using benchmark data. Our time-series gradient boosting tree has weak learners with time-series and cross-sectional attribute in its internal node, and split examples based on dissimilarity between a pair of time-series or impurity between a pair of cross-sectional attributes.It has been empirically observed that the method induces accurate and comprehensive decision trees in time-series classification, which has gaining increasing attention due to its importance in various real-world applications.