Second-generation functional data
Modern studies from a variety of fields record multiple functional observations according to
either multivariate, longitudinal, spatial, or time series designs. We refer to such data as …
either multivariate, longitudinal, spatial, or time series designs. We refer to such data as …
[HTML][HTML] The SPDE approach for spatio-temporal datasets with advection and diffusion
In the task of predicting spatio-temporal fields in environmental science using statistical
methods, introducing statistical models inspired by the physics of the underlying phenomena …
methods, introducing statistical models inspired by the physics of the underlying phenomena …
Cellular traffic prediction: a deep learning method considering dynamic nonlocal spatial correlation, self-attention, and correlation of spatiotemporal feature fusion
Z Rao, Y Xu, S Pan, J Guo, Y Yan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Cellular traffic prediction will play a key role in the deployment of future smart cities.
Although the current traffic prediction methods based on deep learning show better …
Although the current traffic prediction methods based on deep learning show better …
Spatio-temporal DeepKriging for interpolation and probabilistic forecasting
Gaussian processes (GP) and Kriging are widely used in traditional spatio-temporal
modelling and prediction. These techniques typically presuppose that the data are observed …
modelling and prediction. These techniques typically presuppose that the data are observed …
Separable expansions for covariance estimation via the partial inner product
T Masak, S Sarkar, VM Panaretos - Biometrika, 2023 - academic.oup.com
The nonparametric estimation of covariance lies at the heart of functional data analysis,
whether for curve or surface-valued data. The case of a two-dimensional domain poses both …
whether for curve or surface-valued data. The case of a two-dimensional domain poses both …
Fully nonseparable Gneiting covariance functions for multivariate space–time data
We broaden the well-known Gneiting class of space–time covariance functions by
introducing a very general parametric class of fully nonseparable direct and cross …
introducing a very general parametric class of fully nonseparable direct and cross …
Spatio-temporal cross-covariance functions under the Lagrangian framework with multiple advections
When analyzing the spatio-temporal dependence in most environmental and earth sciences
variables such as pollutant concentrations at different levels of the atmosphere, a special …
variables such as pollutant concentrations at different levels of the atmosphere, a special …
Benign overfitting in time series linear model with over-parameterization
S Nakakita, M Imaizumi - arXiv preprint arXiv:2204.08369, 2022 - arxiv.org
The success of large-scale models in recent years has increased the importance of
statistical models with numerous parameters. Several studies have analyzed over …
statistical models with numerous parameters. Several studies have analyzed over …
[HTML][HTML] Modelling multivariate spatio-temporal data with identifiable variational autoencoders
M Sipilä, C Cappello, S De Iaco, K Nordhausen… - Neural Networks, 2025 - Elsevier
Modelling multivariate spatio-temporal data with complex dependency structures is a
challenging task but can be simplified by assuming that the original variables are generated …
challenging task but can be simplified by assuming that the original variables are generated …
Parallel space-time likelihood optimization for air pollution prediction on large-scale systems
Gaussian geostatistical space-time modeling is an effective tool for performing statistical
inference of field data evolving in space and time, generalizing spatial modeling alone at the …
inference of field data evolving in space and time, generalizing spatial modeling alone at the …