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• W4311462009 abstract "The data observed in many phenomena have a spatial and a temporal component. Due to the rapid development of complex, performant technologies, spatio-temporal data can now be collected on a large scale. However, the statistical modeling of large sets of spatio-temporal data involves several challenging problems. For example, it is computationally challenging to deal with large datasets and spatio-temporal nonstationarity. Therefore, the development of novel statistical models is necessary. Here, we present a new methodology to model complex and large spatio-temporal datasets. In our approach, we estimate a continuous surface at each time point, and this captures the spatial dependence, possibly nonstationary. In this way, the spatio-temporal data result in a sequence of surfaces. Then, we model this sequence of surfaces using functional time series techniques. The functional time series approach allows us to obtain a computationally feasible methodology, and also provides extensive flexibility in terms of time-forecasting. We illustrate these advantages through a Monte Carlo simulation study. We also test the performance of our method using a high-resolution wind speed simulated dataset of over 4 million values. Overall, our method uses a new paradigm of data analysis in which the random fields are considered as a single entity, a very valuable approach in the context of big data." @default.
• W4311462009 created "2022-12-26" @default.
• W4311462009 creator A5027166101 @default.
• W4311462009 creator A5081709434 @default.
• W4311462009 date "2023-03-01" @default.
• W4311462009 modified "2023-10-14" @default.
• W4311462009 title "Surface time series models for large spatio-temporal datasets" @default.
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• W4311462009 doi "https://doi.org/10.1016/j.spasta.2022.100718" @default.
• W4311462009 hasPublicationYear "2023" @default.
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