Abstract
In climate science, knowledge about the system mostly relies on measured time series. A common problem of highest interest is the analysis of high-dimensional time series having different phases. Clustering in a multi-dimensional non-stationary time series is challenging since the problem is ill- posed. In this paper, the Finite Element Method of non-stationary clustering is applied to find regimes and the long-term trends in a temperature time series. One of the important attributes of this method is that it does not depend on any statistical assumption and therefore local stationarity of time series is not necessary. Results represent low-frequency variability of temperature and spatiotemporal pattern of climate change in an area despite higher frequency harmonics in time series.
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Sefidmazgi, M.G., Sayemuzzaman, M., Homaifar, A. (2014). Non-stationary Time Series Clustering with Application to Climate Systems. In: Jamshidi, M., Kreinovich, V., Kacprzyk, J. (eds) Advance Trends in Soft Computing. Studies in Fuzziness and Soft Computing, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-319-03674-8_6
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DOI: https://doi.org/10.1007/978-3-319-03674-8_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03673-1
Online ISBN: 978-3-319-03674-8
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