Spatio-Temporal Prediction of a Nonstationary and Nonseparable Random Fields with Tucker Decomposition of Covariance Tensor

Document Type : Original Paper


Department of Statistics, Tarbiat Modares University


In spatio-temporal data analysis, the most common way to consider the spatio-temporal correlation structure of data is to use the covariance function, which is usually unknown and estimated based on observations. This method requires constraints such as stationarity, isotropy and separability for the random field. Although the acceptance of these hypotheses facilitates the fitting of valid models to the spatio-temporal covariance function, they are not necessarily realistic in applied problems. In this paper, to expedite the calculation of spatio-temporal prediction for a non-stationary and non-separable random field, a possible model based on spatial-temporal covariance tensor analysis based on Tucker analysis is investigated. Then, we show the proposed method for predicting wind energy based on spatio-temporal wind speed data at 31 weather stations in Iran.


Main Subjects

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Volume 11, Issue 3
September 2021
Pages 433-445
  • Receive Date: 08 October 2020
  • Revise Date: 25 July 2021
  • Accept Date: 03 August 2021
  • First Publish Date: 07 August 2021