A New Procedure for Generalized STAR Modeling using IAcM Approach

A new procedure of space-time modeling through the Invers of Autocovariance Matrix (IAcM) is proposed. By evaluating the IAcM behaviors on behalf of the Generalized Space-Time Autoregressive (GSTAR) process stationarity, we may find an appropriate model to space-time data series. This method can complete the Space-Time ACF and PACF methods for identifying space-time models. For study case, we apply the GSTAR models to the monthly tea production of some plantations in West Java, Indonesia

Application of Generalized Space-Time Autoregressive Model on GDP Data in West European Countries

This paper provides an application of generalized space-time autoregressive (GSTAR) model on GDP data in West European countries. Preliminary model is identified by space-time ACF and space-time PACF of the sample, and model parameters are estimated using the least square method. The forecast performance is evaluated using the mean of squared forecast errors (MSFEs) based on the last ten actual data. It is found that the preliminary model is GSTAR(2;1,1). As a comparison, the estimation and the forecast performance are also applied to the GSTAR(1;1) model which has fewer parameter. The results showed that the ASFE of GSTAR(2;1,1) is smaller than that of the order (1;1). However, the t-test value shows that the performance is significantly indifferent. Thus, due to the parsimony principle, the GSTAR(1;1) model might be considered as a forecasting model

Generalized Space-Time Autoregressive Modeling of the Vertical Distribution of Copper and Gold Grades with a Porphyry-Deposit Case Study

We examined the first-order application of the generalized space-time autoregressive GSTAR (1;1) model. The autoregressive model was used and was performed simultaneously in multiple drill-hole locations. The GSTAR model was applied to data with absolute time parameter units, such as hours, days, months, or years. Here a new perspective on modeling space-time data is raised. We used the relative time parameter index as a discretization of the same drilling depth of mineralization through a porphyritic deposit. Random variables were the copper and gold grades derived from the hydrothermal fluid that passed through the rock fractures in a porphyry copper deposit in Indonesia. This research aims to model the vertical distribution of copper and gold grades through backcasting the GSTAR (1;1) model. Such results could help geologists to predict copper and gold grades in deeper zones in an ore deposit. Two spatial weight matrices were used in the GSTAR (1;1) model, and these were based on a Euclidean distance and kernel function. Both weight matrices were constructed from different perspectives. The Euclidean distance approach gave a fixed weight matrix. Meanwhile, the kernel function approach gave the possibility to be random since it is based on real observations. It is obtained that the estimated (in-sample) and predicted (out-sample) kernel weight approach was accurate. Copper and gold grades data could recommend the GSTAR (1;1) model with a spatial kernel weight for modeling the vertical continuity case

TWO-DIMENSIONAL LEASE CONTRACT WITH PREVENTIVE MAINTENANCE USING BIVARIATE WEIBULL

This paper develops a two-dimensional lease contract for repairable products. During the contract period, all maintenance actions are carried out by the lessor. There are two customer types considered – the one whose lease contract end because a usage limit and the other type whose lease contract cease because the time limit has reached first. The proposed model uses a two-dimensional approach, and model failures using a bivariate Weibull distribution. When the age or usage of equipment reaches a specified limit, an imperfect preventive maintenance is conducted and each PM will reduce the failure rate level of the equipment.  Furthermore, if the equipment fails, a minimal repair corrective maintenance is performed. As an illustration, a numerical example is presented to show the optimal preventive maintenance that minimizes lessor’s maintenance cost, and the expected number of breakdowns during leased contract period. This proposed model will be compared with a recent relevant approach through numerical computation

ANALISIS DATA BLR DAN EAR DALAM MENGKAJI FENOMENA MJO DAN KETERKAITANNYA DENGAN CURAH HUJAN DI ATAS KOTOTABANG DAN SEKITARNYA

This paper is mainly concerned to the analysis of Madden Julian Oscillation (MJO) phenomena crossing over Kototabang, West Sumatere and surrounding areas using Boundary Layer Radar (BLR) and Equatorial Atmosphere Radar (EAR) data taken from September to December 2001 as cintinuing studied by Indriaty (2005). We are interested to continue, especially on the effect of MJO phenomena on the daily rainfall intensity distribution over Kototabang and surrounding area. We divided data into two steps analysis. First is the vertical profile analysi using BLR and EAR data, and the second step is surface analysis using RAINFALL INTENSITY DATA FROM TAKEN FROM THREE METEOROLOGICAL STATIONS IN wEST sUMATERA. tHEY ARE bADAN mETEOROLOGI DAN gEOFISIKA (bmg) Sicicin station (0.6˚LS; 100.22˚BT), BMG-Padangpanjang station (0.5˚LS; 100.41˚BT) and Statsiun Pengamat Dirgantara (SPD) LAPAN Kototabang (0.2˚LS; 100.32˚BT). The vertical profile of zonal-vertical wind vector of EAR data analysis shows that the pre-dominant wind mived to east direction, especially in surface layer, while in the upper troposhere the pre-dominant wind moved to the opposite direction, especially from September to December 2001. This result looks a siminar with the schematic theory of the MJO cross section along equator that described by Matthews (2000). A simiinar result is also shown by the BLR data analysis. Both EAR and BLR data are siminar each other. Since the MJO phenomena is expected passing over Kototabang around mid of November to mid of December 2001, we are interested to analysis the as already mentioned above using the global wavelet spectrum technique. The result shows that their pre-dominant peak ascillation is about 48 days. This result is consistent with the Outgoing Longwave Radiation (OLR) anomaly taken from infra-red sensor of satellite that already been done by Matthews (2000). While, the cross correlation function (CCF) analysis between zonal wind and rainfall data shows unsignificant (very small) value. We suspect that the surface rainfall intensity over Kototabang and surrounding area is not mainly dominanted by MJO phenomena

Analisis Spasiotemporal Populasi Lalat Sciarid pada Budidaya Jamur Tiram

Sciarid fly (Bradysia ocellaris Comstock) population growth and its distribution in the mushroom house was studied. Insects were collected using sticky yellow traps laid on 21 stations in the house over 18 weeks. At the first time, insects population was low. After that, insect population grew and reached at a maximum level in the 8th week. In subsequent weeks, insect population fluctuated. In the other hand, insect population dispersal also occured in the house. Based on spatiotemporal analysis using contour map and semivariogram analysis, insect population showed aggregation pattern, in a small group is called subpopulation. This was related to biological characters of insect such as eggs oviposition in mass and short flights