Abstract
In this paper, we propose a new algorithm for clustering multi-dimensional time series (MDTS). It is based on the predictive clustering paradigm, which combines elements of predictive modelling and clustering. It builds upon the algorithm for predictive clustering trees for modelling time series, and extends it to model MDTS. We also propose adequate distance functions for modelling MDTS. We apply the newly developed approach to the task of analyzing data on forest growing stock in state-owned forests in Slovenia. This task of high importance, since the growing stock of forest stands is a key feature describing the spatio-temporal dynamics of the forest ecosystem response to natural and anthropogenic impacts. It can be thus used to follow the structural, functional and compositional changes of forest ecosystems, which are of increasing importance as the forest area in Europe has been growing steadily for the last 20 years. We have used two scenarios (quantitative and qualitative) to analyze the data at hand. Overall, the growing stock in Slovenian forests has been increasing in the last 40 years. More specifically, the growing stock of the three tree-size has progressive dynamics, which indicates that Slovenian state-owned forests have balanced structure.
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Acknowledgement
We would like to acknowledge the support of the European Commission through the project MAESTRA - Learning from Massive, Incompletely annotated, and Structured Data (Grant number ICT-2013-612944).
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Gjorgjioski, V., Kocev, D., Bončina, A., Džeroski, S., Debeljak, M. (2017). Predictive Clustering of Multi-dimensional Time Series Applied to Forest Growing Stock Data for Different Tree Sizes. In: Trajanov, D., Bakeva, V. (eds) ICT Innovations 2017. ICT Innovations 2017. Communications in Computer and Information Science, vol 778. Springer, Cham. https://doi.org/10.1007/978-3-319-67597-8_18
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DOI: https://doi.org/10.1007/978-3-319-67597-8_18
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