Spatial Weight Determination of GSTAR(1;1) Model by Using Kernel Function

Yundari, Yundari; Pasaribu, Udjianna S.; Mukhaiyar, Utriweni; Heriawan, Mohamad Nur

doi:10.1088/1742-6596/1028/1/012223

Cited by 18 publications

(14 citation statements)

References 7 publications

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“… Yundari et al (2017) researched error assumptions on the GSTAR. Recently Yundari et al (2018) researched the Spatial Weight Determination of GSTAR(1; 1) by using kernel function. This research made that weight matrix construction was less subjective.…”

Section: Gstar With Modified Inverse Distance – Spatial Weight Matrixmentioning

confidence: 99%

Modelling COVID-19 growth cases of provinces in java Island by modified spatial weight matrix GSTAR through railroad passenger's mobility

Pasaribu¹,

Mukhaiyar²,

Huda³

et al. 2021

Heliyon

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s The movement of positive people Coronavirus Disease that was discovered in 2019 (Covid-19), written 2019-nCoV, from one location to another has a great opportunity to transmit the virus to more people. High-risk locations for transmission of the virus are public transportations, one of which is the train, because many people take turns in or together inside. One of the policies of the government is physical distancing, then followed by large-scale social restrictions. The keys to the policy are distance and movement. The most famous transportation used for the movement of people among provinces on Java is train. Here a Generalized Space Time Autoregressive (GSTAR) model is applied to forecast infected case of 2019-nCoV for 6 provinces in Java. The specialty of this model is the weight matrix as a tool to see spatial dependence. Here, the modified Inverse Distance Weight matrix is proposed as a combination of the population ratio factor with the average distance of an inter-provincial train on the island of Java. The GSTAR model (1; 1) can capture the pattern of daily cases increase in 2019-nCoV, evidenced by representative results, especially in East Java, where the increase in cases is strongly influenced by other provinces on the island of Java. Based on the Mean Squares of Residuals, it is obtained that the modified matrix gives better result in both estimating (in-sample) and forecasting (out-sample) compare with the ordinary matrix.

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Section: Gstar With Modified Inverse Distance – Spatial Weight Matrixmentioning

confidence: 99%

Modelling COVID-19 growth cases of provinces in java Island by modified spatial weight matrix GSTAR through railroad passenger's mobility

Pasaribu¹,

Mukhaiyar²,

Huda³

et al. 2021

Heliyon

View full text Add to dashboard Cite

show abstract

“…These properties are known as the invertibility property of the autoregression model orde 1. The GSTAR(1;1) model is a member of the autoregression model family [3]. The question of research, Is the GSTAR(1;1) model also equivalent to the GSTMA(∞;1) model?…”

Section: Introductionmentioning

confidence: 99%

“…[13] proposes a Fuzzy set approach based on observational data in determining the weight matrix, but the approach still produces the weights assigned is not random. Determining random weight matrix have be be done by author by using some kernel functions approach [3]. Furthermore, the spatial weight effect of the random kernel is also examined for its stationary properties [14].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the spatial weight effect of the random kernel is also examined for its stationary properties [14]. Some of the space-time data applied using the GSTAR model are the tourist number data at several tourist attractions [15], the tea production data [5], the GDP data in the countries in Europe [11], the chili prices prediction [16], the data of log Gamma Ray [3], the rainfall data [10] and so on.…”

Section: Introductionmentioning

confidence: 99%

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Invertibility of Generalized Space-Time Autoregressive Model with Random Weight

Yundari

Rizki

2021

CAUCHY

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The generalized linear process accomplishes stationarity and invertibility properties. The invertibility property must be having a series of convergence conditions of the process parameter. The generalized Space-Time Autoregressive (GSTAR) model is one of the stationary linear models therefore it is necessary to reveal the invertibility through the convergence of the parameter series. This article studies the invertibility of model GSTAR(1;1) with kernel random weight. The result shows that the model GSTAR(1;1) under kernel random weight fulfills the invertibility property and obtains a finite order of Generalized Space-Time Moving Average (GSTMA) process. The other result obtained is the time order of the finite orde . On the Triangular kernel resulted in the relatively great value n, so that it does not apply to the kernel with a finite value n.

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“…The role of the kernel function as a weight function can also be applied to the estimation of the GSTAR model, especially its semiparametric method, which serves as a smoothing of the parametric method results that have been previously obtained. The application of the GSTAR model has been carried out for Gamma-Ray log data on soil layers (Yundari et al, 2018). Rock layers are represented as the index of time parameters in rock layers and can be used according to the law of superposition in rock stratigraphy (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

The Application of the Semiparametric Gstar Model in Determining Gamma-Ray Log Data on Soil Layers

Yundari

Martha

2021

Medstat

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This research examines the semiparametric Generalized Space-Time Autoregressive (GSTAR) spacetime modeling and determines its spatial weight. In general, the spatial weights used are uniform, binary weights, and based on the distance, the result is a fixed weight. The GSTAR model is a stochastic model that takes into account its random variables. Thus, it is necessary to study the random spatial weights. This study introduced a new method to estimate the observed value of the GSTAR model semiparametric with a uniform kernel. The data involved the Gamma Ray (GR) log data on four coal drill holes. The semiparametric GSTAR modeling aimed to predict the amount of log GR in the unobserved soil layer based on the observation data information on the layer above it and its surrounding location. The results revealed that semiparametric GSTAR modeling could predict the presence of coal seams and their thickness of drill holes. The results also highlight the validity test on the out-sample data that the error in each borehole results in a small error. In addition, the error tends to approach the actual observed value at a depth of 1 meter down.

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Spatial Weight Determination of GSTAR(1;1) Model by Using Kernel Function

Cited by 18 publications

References 7 publications

Modelling COVID-19 growth cases of provinces in java Island by modified spatial weight matrix GSTAR through railroad passenger's mobility

Modelling COVID-19 growth cases of provinces in java Island by modified spatial weight matrix GSTAR through railroad passenger's mobility

Invertibility of Generalized Space-Time Autoregressive Model with Random Weight

The Application of the Semiparametric Gstar Model in Determining Gamma-Ray Log Data on Soil Layers

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