Inflation Forecasting for Riau Province: A Comparison of Parametric ARIMA with Nonparametric Nadaraya-Watson Kernel Regression and B-Spline Methods

Wulandari, Nindya; Putri, Rizka Amalia; Nazhirah, Afra; Novia, Siti Arita

doi:10.20956/j.v21i3.43647

Open access Jurnal

Inflation Forecasting for Riau Province: A Comparison of Parametric ARIMA with Nonparametric Nadaraya-Watson Kernel Regression and B-Spline Methods

Jurnal Matematika, Statistika dan Komputasi · Vol. 21 No. 3 (2025) · pp. 763–774

DOI: 10.20956/j.v21i3.43647
ISSN: 2614-8811, 1858-1382
Published: 2025-05-14
Publisher: Department of Mathematics, Hasanuddin University
Language: en
License: creativecommons.org/licenses/by/4.0

Abstract

Forecasting inflation is crucial for assisting in the creation of sound economic strategies, particularly in key regions like Riau Province, one of the country's hubs for the production of crude oil and palm oil. The economic peculiarities of Riau, which set it apart from other regions, lead to inflation patterns in this province that tend to deviate from the national average. Forecasting techniques are crucial for decision-making that promotes stability and overall regional economic planning. The parametric ARIMA method and the nonparametric Nadaraya-Watson Kernel Regression and B-Spline are used in this study to forecast inflation in Riau Province in 2025. While ARIMA is based on certain model assumptions, nonparametric methods are more flexible and can capture more complex patterns. The forecasting results using RMSE, ME, MAE, and MASE showed that the Nadaraya-Watson method performed the best out of the three methods tested. The forecasting results with Nadaraya-Watson Kernel Regression showed a stable decline in inflation, from 0.0464% in March to 0.0191% in August 2025.

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Co-author	Organisation	Joint articles
Erlyne Nadhilah Widyaningrum	Affiliation unknown	2
Morina A. Fathan	Affiliation unknown	2
Erfiani Erfiani	Affiliation unknown	1
Irzaman Irzaman	Affiliation unknown	1
Utami Dyah Syafitri	Affiliation unknown	1
Nur Rezky Safitriani	Affiliation unknown	1
Sitti Nurhaliza	Affiliation unknown	1
Alimatun Najiha	Affiliation unknown	1

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Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en">Inflation Forecasting for Riau Province: A Comparison of Parametric ARIMA with Nonparametric Nadaraya-Watson Kernel Regression and B-Spline Methods</dc:title>
	<dc:creator xml:lang="en">Wulandari, Nindya</dc:creator>
	<dc:creator xml:lang="en">Putri, Rizka Amalia</dc:creator>
	<dc:creator xml:lang="en">Nazhirah, Afra</dc:creator>
	<dc:creator xml:lang="en">Novia, Siti Arita</dc:creator>
	<dc:subject xml:lang="en">ARIMA</dc:subject>
	<dc:subject xml:lang="en">B-Spline</dc:subject>
	<dc:subject xml:lang="en">Kernel regression</dc:subject>
	<dc:subject xml:lang="en">Nadaraya-Watson</dc:subject>
	<dc:description xml:lang="en">Forecasting inflation is crucial for assisting in the creation of sound economic strategies, particularly in key regions like Riau Province, one of the country's hubs for the production of crude oil and palm oil.  The economic peculiarities of Riau, which set it apart from other regions, lead to inflation patterns in this province that tend to deviate from the national average.  Forecasting techniques are crucial for decision-making that promotes stability and overall regional economic planning. The parametric ARIMA method and the nonparametric Nadaraya-Watson Kernel Regression and B-Spline are used in this study to forecast inflation in Riau Province in 2025. While ARIMA is based on certain model assumptions, nonparametric methods are more flexible and can capture more complex patterns. The forecasting results using RMSE, ME, MAE, and MASE showed that the Nadaraya-Watson method performed the best out of the three methods tested. The forecasting results with Nadaraya-Watson Kernel Regression showed a stable decline in inflation, from 0.0464% in March to 0.0191% in August 2025.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2025-05-14</dc:date>
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	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/43647</dc:identifier>
	<dc:identifier>10.20956/j.v21i3.43647</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 21 No. 3 (2025): MAY 2025; 763-774</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/43647/12805</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Jurnal Matematika, Statistika dan Komputasi</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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