Abstract
Defining “clusters” in a time series data is a ubiquitous issue in many engineering problems. We propose an analytical framework for resolving this issue focusing on streamflow time series data. We use an affine jump process and define the number of clusters as the number of specific jumps having jump sizes larger than a prescribed threshold value. Our definition is not only analytically tractable, but also provides a physically-consistent cluster decomposition formula of streamflow time series. Statistical dependence of clusters on the threshold value is analyzed by using real data. We argue transferability of the analysis results to modeling clustered arrivals of water quality load and migratory fish.
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Acknowledgements
The following research grants supported this research: Kurita Water and Environment Foundation 19B018, 20K004, 21K018 and JSPS KAKENHI 19H03073.
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Yoshioka, H., Yoshioka, Y. (2022). Modeling Clusters in Streamflow Time Series Based on an Affine Process. In: Das, B., Patgiri, R., Bandyopadhyay, S., Balas, V.E. (eds) Modeling, Simulation and Optimization. Smart Innovation, Systems and Technologies, vol 292. Springer, Singapore. https://doi.org/10.1007/978-981-19-0836-1_29
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DOI: https://doi.org/10.1007/978-981-19-0836-1_29
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