Time series regression and ARIMAX for forecasting currency flow at Bank Indonesia in Sulawesi region

Suharsono, Agus; Suhartono, Suhartono; Masyitha, Aulia; Anuravega, Arum

doi:10.1063/1.4937107

Cited by 7 publications

(7 citation statements)

References 4 publications

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“…In our case, we are going to focus on modelling the contagion in Jaén in two different ways. The first approach is based on the Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model [1] which, we found, provides better performances than the Autoregressive Integrated Moving Average (ARIMA) (on the same line, see also [2][3][4][5][6]). This is a common model in time series forecasting and is often adopted in finance [7][8][9][10].…”

Section: Introductionmentioning

confidence: 94%

Modeling COVID-19 pandemic with financial markets models: The case of Jaén (Spain)

Guerrero¹,

Galiano²,

Orlando³

2023

Preprint

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The main objective of this work is to test whether some stochastic models typically used in financial markets could be applied to the COVID-19 pandemic. To this end we have implemented the ARIMAX and Cox-Ingersoll-Ross (CIR) models originally designed for interest rate pricing but transformed by us into a forecasting tool. For the latter, which we denoted CIR*, both the Euler-Maruyama method and the Milstein method were used. Forecasts obtained with the maximum likelihood method have been validated with 95% confidence intervals and with statistical measures of goodness of fit, such as the root mean square error (RMSE). We demonstrate that the accuracy of the obtained results is consistent with the observations and sufficiently accurate to the point that the proposed CIR* framework could be considered a valid alternative to the classical ARIMAX for modelling pandemics.

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Section: Introductionmentioning

confidence: 94%

Modeling COVID-19 pandemic with financial markets models: The case of Jaén (Spain)

Guerrero¹,

Galiano²,

Orlando³

2023

Preprint

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“…Regresi deret waktu adalah regresi berganda yang diterapkan untuk menganalisis hubungan antara variabel dependen , 1,2, , [10,11]. Bentuk umum dari regresi deret waktu dengan variasi kalender yang memuat tren t dan pola musiman dinyatakan pada persamaan (2), yaitu…”

Section: Regresi Deret Waktuunclassified

“…Wei [13] mengusulkan teknik identifikasi melalui struktur otokorelasi pada plot ACF untuk mengetahui eksistensi pola musiman. Muhammad Luthfi Setiarno Putera Model ARIMAX (ARIMA with Exogenous) merupakan pengembangan ARIMA dengan keterlibatan variabel independen (kovariat/X) untuk memodelkan variabel Y [11]. Penelitian ini menawarkan beberapa variabel dummy dan variabel kontinyu yang mewakili tren deterministik.…”

Section: Model Arima Dan Arimaxunclassified

Non-cash Payment Transaction Projection Using ARIMAX : Efect of Calendar

Putera

2020

JMSK

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As the most Moslem country, economic activity in Indonesia is often parallel with the movement of Qamariah (lunar) calendar which is different with Gregorian calendar. Using calender variation, this research attempts to look for modified time series model for non-cash payment projection (forecast) aim. The result shows that calendar variation plays statistically significant role on non-cash payment, evidenced by significant payment in the month in which Eid Fitr occurs. The occurrence of Eid Fitr in the first and second week of the month is evidently characterized by increasing non-cash payment in one month earlier. The best model with highest accuracy for non-cash payment projection is ARIMAX(2,1,1) as it is able to capture the pattern, trend and fluctuation. It also suggests the peak of non-cash payment will be in December.

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“…Dalam [13], ARIMAX dapat meningkatkan akurasi peramalan regresi deret waktu maupun ARIMA. ARIMAX merupakan model deret waktu ARIMA yang dikembangkan dengan melibatkan variasi kalender sebagai variabel penjelas (independen) [1]. Akurasi peramalan dapat ditingkatkan dengan mengkombinasikan aspek linier dalam ARIMAX dan aspek non-linier.…”

Section: Pendahuluanunclassified

Peramalan Transaksi Pembayaran Non-Tunai Menggunakan Arimax-Ann Dengan Konfigurasi Kalender

Putera

2020

BAREKENG

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Akses internet yang luas mendorong kian seringnya sistem pembayaran non-tunai digunakan. Di Indonesia, berbagai aktivitas dan transaksi ekonomi seringkali dipengaruhi oleh pergerakan kalender, terutama kalender Hijriyah. Tujuan penelitian ini untuk memodelkan dan meramalkan total pembayaran non-tunai di Indonesia dengan menambahkan konfigurasi kalender sebagai variabel. Digunakan metode ARIMA, ARIMAX dan hibrida ARIMAX-ANN yang akan dibandingkan akurasinya. Diperoleh model terbaik untuk peramalan jumlah pembayaran non-tunai adalah ARIMAX-ANN dengan RMSE terkecil, yaitu Rp 20,9 triliun. Spesifikasi model terbaik tersebut adalah ARIMAX(2,1,1) yang dihibrida dengan ANN yang inputnya diseleksi melalui regresi stepwise. Selain memenuhi asumsi galat yang identik, independen, dan berdistribusi normal, ARIMAX-ANN juga mampu mengikuti dinamika dan tren dari pembayaran non-tunai, khususnya pada bulan jatuhnya Idul Fitri dan periode akhir tahun.

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Time series regression and ARIMAX for forecasting currency flow at Bank Indonesia in Sulawesi region

Cited by 7 publications

References 4 publications

Modeling COVID-19 pandemic with financial markets models: The case of Jaén (Spain)

Modeling COVID-19 pandemic with financial markets models: The case of Jaén (Spain)

Non-cash Payment Transaction Projection Using ARIMAX : Efect of Calendar

Peramalan Transaksi Pembayaran Non-Tunai Menggunakan Arimax-Ann Dengan Konfigurasi Kalender

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