A Three-Stage Iterative Procedure for Space-Time Modeling

Pfeifer, Phillip E.; Deutsch, Sid

doi:10.2307/1268381

Cited by 307 publications

(212 citation statements)

References 13 publications

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“…STAR model was introduced by [9], defined as a lag spatial operator to express the influence of the nearest neighbors at a certain spatial lag through the use of weights. The equation of STAR model can be written as: ( 1 ) where:…”

Section: Literature Review Space Time Autoregressive (Star) Modelmentioning

confidence: 99%

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Development of generalized space time autoregressive integrated with ARCH error (GSTARI – ARCH) model based on consumer price index phenomenon at several cities in North Sumatera province

Bonar¹,

Ruchjana²,

Darmawan³

2017

AIP Conference Proceedings

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Section: Literature Review Space Time Autoregressive (Star) Modelmentioning

confidence: 99%

“…One of data modeling methods that can be used to model space time data which was introduced by [1] and [9], is Space Time Autoregressive (STAR) model. STAR model assumes that the characteristic for all observation locations are homogenous, but in reality we found heterogenous condition.…”

Section: Introductionmentioning

confidence: 99%

Development of generalized space time autoregressive integrated with ARCH error (GSTARI – ARCH) model based on consumer price index phenomenon at several cities in North Sumatera province

Bonar¹,

Ruchjana²,

Darmawan³

2017

AIP Conference Proceedings

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“…Parametric models assume that the data follow a particular parametric distribution, and that the future values of the process can be modelled as a (usually linear) combination of its past values. Examples of parametric space-time forecasting models include the space-time autoregressive integrated moving average (STARIMA) modelling framework ( (Pfeifer and Deutsch, 1980); (Kamarianakis and Prastacos, 2005), statespace models ( (Stathopoulos and Karlaftis, 2003), Bayesian networks (Sun et al, 2005) to name but a few.…”

Section: Space-time Forecastingmentioning

confidence: 99%

A Comparison of Neighbourhood Selection Techniques in Spatio-Temporal Forecasting Models

Haworth

Cheng

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Spatio-temporal neighbourhood (STN) selection is an important part of the model building procedure in spatio-temporal forecasting. The STN can be defined as the set of observations at neighbouring locations and times that are relevant for forecasting the future values of a series at a particular location at a particular time. Correct specification of the STN can enable forecasting models to capture spatio-temporal dependence, greatly improving predictive performance. In recent years, deficiencies have been revealed in models with globally fixed STN structures, which arise from the problems of heterogeneity, nonstationarity and nonlinearity in spatio-temporal processes. Using the example of a large dataset of travel times collected on London's road network, this study examines the effect of various STN selection methods drawn from the variable selection literature, varying from simple forward/backward subset selection to simultaneous shrinkage and selection operators. The results indicate that STN selection methods based on penalisation are effective. In particular, the maximum concave penalty (MCP) method selects parsimonious models that produce good forecasting performance.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

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“…To overcome this problem, a simple extension is to let neighbouring spatial locations at previous time contribute to the process at current time. This model structure is known as nearest-neighbour VAR model [1] and constitutes a particular form of the STAR models [10] . In a different manner from Wikle et al [1] , the nearest-neighbour model relies on a constrained transition matrix which allows temporal relations only for the spatially contiguous sites.…”

Section: Nearest-neighbour Var Model In the Observed Data Domainmentioning

confidence: 99%

Parsimonious Var Models For Air Pollution Dynamic Analysis

Lara¹,

Mariagraz²

2005

J. of Mathematics and Statistics

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Abstract:We discuss a framework to obtain temporal predictions for an evolving spatial field regularly sampled in time at arbitrary spatial locations. Difficulties caused by large data sets and the modelling of complicated spatio-temporal interactions limit the effectiveness of traditional space-time statistical models. In this study, we propose the use of a flexible approach to deal with large and small time-scale variability of the observed data. The temporal model is applied with respect to both the observed data domain and the common component domain, to achieve a dimensionality reduction.

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A Three-Stage Iterative Procedure for Space-Time Modeling

Cited by 307 publications

References 13 publications

Development of generalized space time autoregressive integrated with ARCH error (GSTARI – ARCH) model based on consumer price index phenomenon at several cities in North Sumatera province

Development of generalized space time autoregressive integrated with ARCH error (GSTARI – ARCH) model based on consumer price index phenomenon at several cities in North Sumatera province

A Comparison of Neighbourhood Selection Techniques in Spatio-Temporal Forecasting Models

Parsimonious Var Models For Air Pollution Dynamic Analysis

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