Hierarchically spatial autoregressive and moving average error model

Abstract. Regresi spasial merupakan pengembangan dari regresi klasik. Pengembangan ini berdasarkan adanya pengaruh tempat atau spasial dari data yang dianalisis. Beberapa model regresi spasial adalah Spatial Autoregressive (SAR), Spatial Error Model (SEM) dan Spatial Moving Average (SARMA). Penelitian ini menggunakan analisis model SAR terhadap angka putus sekolah di Sulawesi Selatan. Data yang digunakan merupakan data sekunder dari Badan Pusat Statistik Provinsi Sulawesi Selatan tahun 2018. Penelitian ini dilakukan untuk mengetahui model Spatial Autoregressive (SAR) pada data banyaknya angka putus sekolah yang terjadi di Provinsi Sulawesi Selatan, serta mengenalisis faktor-faktor yang memberikan pengaruh signifikan terhadap pertumbuhan angka putus sekolah. Hasil penelitian ini memperoleh model yaitu ; sehingga faktor-faktor yang berpengaruh secara signifikan terhadap angka putus sekolah di Sulawesi Selatan adalah pengeluaran per kapita, rasio murid terhadap sekolah dan jumlah penduduk miskin.Keywords: Regresi Spasial, Spatial Autoregressive Model (SAR), Angka Putus Sekolah

Download Full-text

Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

Frontiers in Veterinary Science ◽

10.3389/fvets.2021.775114 ◽

2021 ◽

Vol 8 ◽

Author(s):

Veerasak Punyapornwithaya ◽

Katechan Jampachaisri ◽

Kunnanut Klaharn ◽

Chalutwan Sansamur

Keyword(s):

Milk Production ◽

Hybrid Model ◽

Moving Average ◽

Seasonal Fluctuation ◽

Predictive Ability ◽

Northern Region ◽

Average Error ◽

Autoregressive Integrated Moving Average ◽

Time Series Forecast ◽

Forecast Models

Milk production in Thailand has increased rapidly, though excess milk supply is one of the major concerns. Forecasting can reveal the important information that can support authorities and stakeholders to establish a plan to compromise the oversupply of milk. The aim of this study was to forecast milk production in the northern region of Thailand using time-series forecast methods. A single-technique model, including seasonal autoregressive integrated moving average (SARIMA) and error trend seasonality (ETS), and a hybrid model of SARIMA-ETS were applied to milk production data to develop forecast models. The performance of the models developed was compared using several error matrices. Results showed that milk production was forecasted to raise by 3.2 to 3.6% annually. The SARIMA-ETS hybrid model had the highest forecast performances compared with other models, and the ETS outperformed the SARIMA in predictive ability. Furthermore, the forecast models highlighted a continuously increasing trend with evidence of a seasonal fluctuation for future milk production. The results from this study emphasizes the need for an effective plan and strategy to manage milk production to alleviate a possible oversupply. Policymakers and stakeholders can use our forecasts to develop short- and long-term strategies for managing milk production.

Download Full-text

PEMODELAN JUMLAH TINDAK KRIMINALITAS DI PROVINSI JAWA TIMUR DENGAN ANALISIS REGRESI SPATIAL AUTOREGRESSIVE AND MOVING AVERAGE

E-Jurnal Matematika ◽

10.24843/mtk.2018.v07.i04.p224 ◽

2018 ◽

Vol 7 (4) ◽

pp. 346

Author(s):

NI MADE LASTI LISPANI ◽

I WAYAN SUMARJAYA ◽

I KOMANG GDE SUKARSA

Keyword(s):

Population Density ◽

Regression Model ◽

Average Length ◽

Moving Average ◽

Spatial Regression ◽

Higher Order ◽

Spatial Effect ◽

Spatial Regression Model ◽

Spatial Autoregressive ◽

Total Average

One of spatial regression model is spatial autoregressive and moving average (SARMA) which assumes that there is a spatial effect on dependent variable and error. SARMA can analyze the spatial effect on the higher order. The purpose of this research is to estimate the model of the total crime in East Java along with factors that affect it. The results show that the model can describe total crime in East Java is SARMA(0,1). The factors that influence the total crime are population density (), poverty total (), average length of education at every regency/city and error from the neigbors.

Download Full-text

Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with a Moving Average Disturbance Term

Econometrics ◽

10.3390/econometrics3010101 ◽

2015 ◽

Vol 3 (1) ◽

pp. 101-127 ◽

Cited By ~ 1

Author(s):

Osman Doğan

Keyword(s):

Moving Average ◽

Autoregressive Models ◽

Disturbance Term ◽

Spatial Autoregressive ◽

Unknown Form ◽

Spatial Autoregressive Models

Download Full-text

Model Regresi Spasial dan Aplikasinya pada Kasus Tingkat Kemiskinan Kabupaten Soppeng

Indonesian Journal of Fundamental Sciences ◽

10.26858/ijfs.v4i2.7638 ◽

2018 ◽

Vol 4 (2) ◽

pp. 102

Author(s):

Anggi Ananda Putri ◽

Wahidah Sanusi ◽

Sukarna Sukarna

Keyword(s):

Social Issues ◽

Well Being ◽

Error Model ◽

Poor Household ◽

Spatial Dependency ◽

Spatial Error Model ◽

Spatial Error ◽

Sar Model ◽

Spatial Autoregressive ◽

Independent Variable

Poverty is one of the major problem that frequently faced by human. Begin from poverty, consequently emerged several social issues, such as homeless, beggar, defendant, and prostitution. On this research were conducted modeling poverty degree in Soppeng with using number of poor household as the dependent variable. Modeling were done by using area approach which is a Spatial Autoregressive (SAR) model and Spatial Error Model (SEM). As for the independent variable used on this research is the number of health services, school facility, population density, social well being disable, and the distance on village and centre of Soppeng. Regarding to the analysis of Spatial Autoregressive (SAR) and Spatial Error Model (SEM) shows that there is a spatial dependency lag and error on number of poor household variable. As for the independent variable which have the significancy account for 5% on Spatial Autoregressive (SAR) and Spatial Error Model (SEM) are every variables with a number R2= 90,9% on SAR and R2= 90,1% on SEM.

Download Full-text

PENGGUNAAN ANALISIS KLASTER K-MEANS DALAM PEMODELAN REGRESI SPASIAL PADA KASUS TUBERKULOSIS DI JAWA TIMUR TAHUN 2017

Indonesian Journal of Statistics and Its Applications ◽

10.29244/ijsa.v4i1.563 ◽

2020 ◽

Vol 4 (1) ◽

pp. 164-178

Author(s):

Hardani Prisma Rizky ◽

Wara Pramesti ◽

Gangga Anuraga

Keyword(s):

Moving Average ◽

Spatial Regression ◽

P Value ◽

Autoregressive Moving Average ◽

Test Results ◽

Tuberculosis Patients ◽

Weighting Matrix ◽

Regression Approach ◽

Spatial Dependencies ◽

Spatial Autoregressive

Tuberculosis (TB) is a contagious infectious disease caused by the bacterium Mycobacterium tuberculosis which can attack various organs, especially the lungs. TB if left untreated or incomplete treatment can cause dangerous complications to death. East Java Province has the second-highest TB case after West Java Province. Therefore we need statistical modeling to analyze the factors that influence TB in East Java Province. The data used in this study were sourced from data from BPS and East Java Provincial Health Offices in 38 districts/cities in East Java Province in 2017. Analysis of data using the OLS regression approach only looked at variable factors but was unable to know the effects of territory. So to overcome this, a spatial regression approach is used by comparing the weight of Queen Contiguity and the results of the k-means cluster analysis to obtain the best model. Based on the results of the analysis, the spatial aspects of the data have met the assumptions of spatial dependencies using the Moran's I test with a p-value of 0.000001295. The weighting matrix used is the k-means cluster weighting matrix k = 2. The test results obtained by the Spatial Autoregressive Moving Average (SARMA) model selected as the best model with the value of the deterrence coefficient (R2) and Akaike Info Criterion (AIC), 87.10% and 586.69. The factors that significantly influence the number of Tuberculosis patients in each district/city in East Java are population density (X2) and the number of healthy houses (X9).

Download Full-text

Prediksi Belanja Pemerintah Indonesia Menggunakan Long Short-Term Memory (LSTM)

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i1.2815 ◽

2021 ◽

Vol 5 (1) ◽

pp. 99-106

Author(s):

Sabar Sautomo ◽

Hilman Ferdinandus Pardede

Keyword(s):

Mean Square Error ◽

Short Term Memory ◽

Moving Average ◽

Government Expenditure ◽

Average Error ◽

Mean Square ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

The Government

Abstract Estimates of government expenditure for the next period are very important in the government, for instance for the Ministry of Finance of the Republic of Indonesia, because this can be taken into consideration in making policies regarding how much money the government should bear and whether there is sufficient availability of funds to finance it. As is the case in the health, education and social fields, modeling technology in machine learning is expected to be applied in the financial sector in government, namely in making modeling for spending predictions. In this study, it is proposed the application of Long Short-Term Memory (LSTM) Model for expenditure predictions. Experiments show that LSTM model using three hidden layers and the appropriate hyperparameters can produce Mean Square Error (MSE) performance of 0.2325, Root Mean Square Error (RMSE) of 0.4820, Mean Average Error (MAE) of 0.3292 and Mean Everage Presentage Error (MAPE) of 0.4214. This is better than conventional modeling using the Auto Regressive Integrated Moving Average (ARIMA) as a comparison model.

Download Full-text

Analysis of Object Location Accuracy for iBeacon Technology based on the RSSI Path Loss Model and Fingerprint Map

International Journal of Electronics and Telecommunications ◽

10.1515/eletel-2016-0051 ◽

2016 ◽

Vol 62 (4) ◽

pp. 371-378 ◽

Cited By ~ 10

Author(s):

Damian E. Grzechca ◽

Piotr Pelczar ◽

Lukas Chruszczyk

Keyword(s):

Moving Average ◽

Path Loss ◽

Propagation Model ◽

Object Location ◽

Line Of Sight ◽

Average Error ◽

Final Conclusion ◽

Location Accuracy ◽

Loss Model ◽

Path Loss Model

Abstract This paper presents analysis of object location accuracy of a mobile device on the basis of the iBeacon technology. The research starts with radio signal strength indicator analysis along the corridor in order to create a path loss model for iBeacon. Two cases are taken into account: line of sight and non-line of sight for model creation. For both cases two tests: Chi-square, Shapiro-Wilk have been performed. It has also been checked if the HCI (Host Controller Interface) is a source with a memory. Acquired data have been filtered with different type of filters, e.g. median, moving average and then compared. Next, the authors evaluated the indoor positioning trilateration algorithms with the use of created model for exemplary hall. The RSSI map (radiomap) was created and the logarithm propagation model was designed. The logarithmic model estimated distance with average error 1.09m for 1 – 9m and 1.75m for 1-20m and after trilateration, the positions with average error 2.45m was achieved. A statistical analysis for acquiring data led to the final conclusion which enhanced knowledge about positioning based on the popular iBeacon technology.

Download Full-text