Abstract
This article is addressed to the problem of modeling and exploring time series with mean value structure of large scale time series data and time-space data. A smoothness priors modeling approach [11] is taken and applied to POS and GPS data. In this approach, the observed series are decomposed into several components each of which are expressed by smoothness priors models. In the analysis of POS and GPS data, various useful information were extracted by this decomposition, and result in some discoveries in these areas.
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Kitagawa, G., Higuchi, T., Kondo, F.N. (1999). Smoothness Prior Approach to Explore the Mean Structure in Large Time Series Data. In: Arikawa, S., Furukawa, K. (eds) Discovery Science. DS 1999. Lecture Notes in Computer Science(), vol 1721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46846-3_21
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DOI: https://doi.org/10.1007/3-540-46846-3_21
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