In this paper we propose modeling technique, which was applied to multivariate time series data that correspond to different spatial locations (spatial time series). ARIMA model class is considered for each location. Forecasting model for new location is built by spatial "connection" of identified models in observed locations. Spatial "connection" is implemented by spatial averaging of the coefficients of models and by ordinary kriging procedure for means. This modeling technique is illustrated by a substantive example using R system.Straipsnyje aprašyta nauja erdvinių laiko eilučių modeliavimo technika, kuriuos esminis principas – erdvinis modelių sujungimas. Siūloma technika lengvai realizuojama laisvai platinamos sistemos R pagalba. Mo...
O objetivo deste trabalho é comparar as estruturas de vizinhanças espaciais ou matrizes de pesos esp...
In this article we have used wide applicable classes of spatio‐temporal nonseparable and separable c...
The aim of this paper is to find a modeling approach for spatially and temporally structured data. T...
This paper discusses three modelling techniques, which apply to multiple time series data that corre...
Spatial time series model for wind speed data is proposed. Based on few similar papers, first at eac...
Bakalaura darbā aplūkota programmas R veiktspēja apstrādāt, analizēt un attēlot telpiska veida datus...
U ovom radu se upoznajemo s prostorno-vremenskim podacima kao proširenje vremenskih nizova, definira...
Klasikiniai ekonometrijos modeliai tampa neefektyvūs kuomet tiriami erdviniai duomenys. Tokiais atve...
We introduce an extension of R-vine copula models for the purpose of spatial dependency modeling and...
Two state–space representations, also known as state–space models (SSMs), are proposed to estimate d...
This is a Rmarkdown tutorial that discusses some aspects of spatial and spatiotemporal data and demo...
The goal of this thesis is to introduce basic methods of prediction of time series and to compare su...
This thesis focuses on the time series in addition to being observed over time, also have a spatial ...
Abstract: Automatic forecasts of univariate time series are largely demanded in business and science...
This thesis is devoted to the models that are suitable for modelling spatial data. For this purpose,...
O objetivo deste trabalho é comparar as estruturas de vizinhanças espaciais ou matrizes de pesos esp...
In this article we have used wide applicable classes of spatio‐temporal nonseparable and separable c...
The aim of this paper is to find a modeling approach for spatially and temporally structured data. T...
This paper discusses three modelling techniques, which apply to multiple time series data that corre...
Spatial time series model for wind speed data is proposed. Based on few similar papers, first at eac...
Bakalaura darbā aplūkota programmas R veiktspēja apstrādāt, analizēt un attēlot telpiska veida datus...
U ovom radu se upoznajemo s prostorno-vremenskim podacima kao proširenje vremenskih nizova, definira...
Klasikiniai ekonometrijos modeliai tampa neefektyvūs kuomet tiriami erdviniai duomenys. Tokiais atve...
We introduce an extension of R-vine copula models for the purpose of spatial dependency modeling and...
Two state–space representations, also known as state–space models (SSMs), are proposed to estimate d...
This is a Rmarkdown tutorial that discusses some aspects of spatial and spatiotemporal data and demo...
The goal of this thesis is to introduce basic methods of prediction of time series and to compare su...
This thesis focuses on the time series in addition to being observed over time, also have a spatial ...
Abstract: Automatic forecasts of univariate time series are largely demanded in business and science...
This thesis is devoted to the models that are suitable for modelling spatial data. For this purpose,...
O objetivo deste trabalho é comparar as estruturas de vizinhanças espaciais ou matrizes de pesos esp...
In this article we have used wide applicable classes of spatio‐temporal nonseparable and separable c...
The aim of this paper is to find a modeling approach for spatially and temporally structured data. T...