@inproceedings{zhang_segmentation_2017,
	title = {A segmentation technology for multivariate contextual time series},
	doi = {10.1109/ISCMI.2017.8279600},
	abstract = {A time series is a series of data points indexed in time order, mining multivariate contextual time series (MCTS) should pay more attention to time order. This paper proposes a new method for splitting the MCTS into a number of segments, uses the concept of scenarios and themes to represent MCTS instead of data points and extracts important contextual features to carry out the multidimensional fitting for MCTS.},
	booktitle = {2017 {IEEE} 4th {International} {Conference} on {Soft} {Computing} {Machine} {Intelligence} ({ISCMI})},
	author = {Zhang, Hui-Juan and Huang, Jia-Cheng},
	month = nov,
	year = {2017},
	keywords = {Conferences, Data mining, Feature extraction, Fitting, Knowledge discovery, Legged locomotion, MCTS, Time series analysis, contextual, contextual features, data analysis, data mining, data points, multidimensional fitting, multivariate contextual time series mining, segmentation, segmentation technology, time order, time series},
	pages = {71--74},
}

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