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pro vyhledávání: '"Oriona, Ángel López"'
In this paper, a novel method to perform model-based clustering of time series is proposed. The procedure relies on two iterative steps: (i) K global forecasting models are fitted via pooling by considering the series pertaining to each cluster and (
Externí odkaz:
http://arxiv.org/abs/2305.00473
Time series data are ubiquitous nowadays. Whereas most of the literature on the topic deals with real-valued time series, categorical time series have received much less attention. However, the development of data mining techniques for this kind of d
Externí odkaz:
http://arxiv.org/abs/2304.12332
The 21st century has witnessed a growing interest in the analysis of time series data. Whereas most of the literature on the topic deals with real-valued time series, ordinal time series have typically received much less attention. However, the devel
Externí odkaz:
http://arxiv.org/abs/2304.12251
Time series clustering is a central machine learning task with applications in many fields. While the majority of the methods focus on real-valued time series, very few works consider series with discrete response. In this paper, the problem of clust
Externí odkaz:
http://arxiv.org/abs/2304.12249