@article {1421,
	title = {Randomized trees for time series representation and similarity},
	journal = {Pattern Recognition},
	volume = {120},
	year = {2021},
	pages = {108097},
	abstract = {<p>Most of the temporal data mining tasks require representations to capture important characteristics of time series. Representation learning is challenging when time series differ in distributional characteristics and/or show irregularities such as varying lengths and missing observations. Moreover, when time series are multivariate, interactions between variables should be modeled efficiently. This study proposes a unified, flexible time series representation learning framework for both univariate and multivariate time series called Rand-TS. Rand-TS models density characteristics of each time series as a time-varying Gaussian distribution using random decision trees and embeds density information into a sparse vector. Rand-TS can work with time series of various lengths and missing observations, furthermore, it allows using customized features. We illustrate the classification performance of Rand-TS on 113 univariate, 19 multivariate along with 15 univariate time series with varying lengths from UCR database. The results show that in addition to its flexibility, Rand-TS provides competitive classification performance.</p>},
	keywords = {Classification, Random trees, Representation learning, Time series},
	issn = {0031-3203},
	doi = {https://doi.org/10.1016/j.patcog.2021.108097},
	url = {https://www.sciencedirect.com/science/article/pii/S0031320321002843},
	author = {Berk G{\"o}rg{\"u}l{\"u} and Mustafa Gokce Baydogan}
}
