Temperature prediction based on a space–time regression-kriging model

DOI10.1080/02664763.2019.1671962OpenAlexW2975444298WikidataQ127200901 ScholiaQ127200901MaRDI QIDQ5037100

Sha Li, Hong Shu, Daniel A. Griffith

Publication date: 25 February 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2019.1671962

zbMATH Keywords

geostatisticsmultiple linear regressionmonthly temperature predictionspace-time krigingspatio-temporal variogram

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Related Items (1)

Spatial modeling of day-within-year temperature time series: an examination of daily maximum temperatures in Aragón, Spain

Uses Software

• R
• Gstat
• gstat

Cites Work

• Unnamed Item
• Time series forecasting using a hybrid ARIMA and neural network model
• The linear coregionalization model and the product-sum space-time variogram
• Recent developments on the construction of spatio-temporal covariance models
• Families of spatio-temporal stationary covariance models.
• Nonseparable space-time covariance models: Some parametric families
• Nonseparable, Stationary Covariance Functions for Space–Time Data
• Classes of Nonseparable, Spatio-Temporal Stationary Covariance Functions
• Space–time correlation analysis: a comparative study
• Space–Time Covariance Functions
• Estimating and modeling space-time correlation structures.

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