Comparison between GSTAR and GSTAR-Kalman Filter models on inflation rate forecasting in East Java

Prillantika, Jessica Rahma; Apriliani, Erna; Wahyuningsih, Nuri

doi:10.1088/1742-6596/974/1/012039

Cited by 2 publications

(2 citation statements)

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“…The inverse distance weight matrix between the i-th location and j-th location is defined as follows: (Maria, Budiman, Haviluddin, & Taruk, 2020;Prillantika et al, 2018):…”

Section: Inverse Distance Weight Matrixmentioning

confidence: 99%

Estimating and Forecasting Covid-19 Cases in Sulawesi Island Using Generalized Space-Time Autoregressive Integrated Moving Average Model

Sukarna

Syahrul

Sanusi

et al. 2023

Medstat

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A range of spatio-temporal models has been used to model Covid-19 cases. However, there is only a small amount of literature on the analysis of estimating and forecasting Covid-19 cases using the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) model. This model is a development of the GSTARMA model which has non-stationary data. This paper aims to estimate and forecast the daily number of Covid-19 cases in Sulawesi Island using GSTARIMA models. We compared two models namely GSTARI and GSTIMA considering the root mean square error (RMSE). Data on a daily number of Covid-19 cases (from April 10, 2020, to May 07, 2021) were used. The location weight used is the inverse distance weight based on the distance between airports in the capital cities of each province. The appropriate models obtained based on the data are the GSTARIMA (1;0;1;1) model and the GSTARIMA (1;1;1;0) model. The results showed that the forecast for the number of new Covid-19 cases is accurate and reliable only for the short term.

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“…The inverse distance weight matrix between the i-th location and j-th location is defined as follows: (Maria, Budiman, Haviluddin, & Taruk, 2020;Prillantika et al, 2018):…”

Section: Inverse Distance Weight Matrixmentioning

confidence: 99%

Estimating and Forecasting Covid-19 Cases in Sulawesi Island Using Generalized Space-Time Autoregressive Integrated Moving Average Model

Sukarna

Syahrul

Sanusi

et al. 2023

Medstat

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“…Metode yang digunakan untuk menentukan pembobotan dalam model GSTAR menggunakan pembobotan invers jarak antar lokasi dan pembobotan normalisasi korelasi silang [7]. Pembobotan invers jarak tertera sebagai berikut [8]:…”

Section: Pembobotan Lokasiunclassified

Estimasi Parameter Model Inflasi untuk Menganalisa Pengaruh Covid-19 Menggunakan GSTAR-Filter Kalman

Agustina¹,

Surjanto²,

Apriliani³

2022

JSSITS

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Abstrak-Pandemi Covid-19 selain mengganggu kesehatan manusia juga dapat mengganggu kesehatan ekonomi di seluruh dunia termasuk Indonesia. Dengan keadaan ekonomi yang tidak stabil akhir-akhir ini, permasalahan inflasi menjadi salah satu fokus penting bagi pemerintah. Inflasi merupakan salah satu indikator penting dalam stabilitas perekonomian bagi suatu negara. Oleh karena itu, perlu adanya pemodelan matematika yang sesuai yang dapat memprediksi inflasi di masa mendatang. Pengaruh Covid-19 terhadap inflasi dapat diamati dengan memperhatikan pergerakan inflasi terhadap Covid-19 berdasarkan plot data inflasi. Selanjutnya data inflasi dimodelkan menggunakan model Generalized Space Time Autoregressive (GSTAR) dengan menggunakan pembobotan invers jarak antar lokasi dan pembobotan normalisasi korelasi silang untuk mendapatkan model inflasi yang sesuai. Selanjutnya dilakukan estimasi pada parameter model menggunakan metode Filter Kalman (FK). Hasil akhir menunjukkan bahwa Filter Kalman mampu memperbaiki hasil estimasi pada model GSTAR sehingga didapatkan hasil prediksi yang mendekati data aktual. Hal ini ditunjukkan dengan hasil simulasi dan nilai MAPE yang lebih kecil dari pada nilai MAPE model GSTAR-OLS dan GSTAR-GLS sebesar 0.14302%.

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Comparison between GSTAR and GSTAR-Kalman Filter models on inflation rate forecasting in East Java

Cited by 2 publications

References 1 publication

Estimating and Forecasting Covid-19 Cases in Sulawesi Island Using Generalized Space-Time Autoregressive Integrated Moving Average Model

Estimating and Forecasting Covid-19 Cases in Sulawesi Island Using Generalized Space-Time Autoregressive Integrated Moving Average Model

Estimasi Parameter Model Inflasi untuk Menganalisa Pengaruh Covid-19 Menggunakan GSTAR-Filter Kalman

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