Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 2

A Dual-Network iTransformer Model for Robust and Efficient Time Series Forecasting

JUTI: Jurnal Ilmiah Teknologi Informasi · Vol. 23 No. 2 (2025) · pp. 1–12

DOI
10.12962/j24068535.v23i2.a1264
ISSN
2406-8535, 1412-6389
Published
2025-07-08
Publisher
Institut Teknologi Sepuluh Nopember
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Time-series forecasting plays a crucial role in various fields, including economics, healthcare, and meteorology, where accurate predictions are essential for informed decision-making. As data volume and complexity continue to grow, the need for efficient and reliable forecasting methods has become more critical. iTransformer, a recent innovation, improves interpretability while effectively handling multivariate data. In this study, the author proposes Dual-Net iTransformer, a novel approach that integrates iTransformer with a dual-network framework to enhance both accuracy and efficiency in time-series forecasting. This research aims to evaluate and compare the performance of traditional methods, iTransformer, and Dual-Net iTransformer, highlighting the advantages of the proposed model in improving forecasting outcomes.

Collaboration network of the authors

40 authors · 104 co-authorship links · 2 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Institut Teknologi Sepuluh Nopember
  • University of Indonesia
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Supeno DjanaliInstitut Teknologi Sepuluh Nopember3
Wahyu SuadiInstitut Teknologi Sepuluh Nopember3
Rully SoelaimanInstitut Teknologi Sepuluh Nopember2
Suhadi LiliInstitut Teknologi Sepuluh Nopember2
Muhammad Raihan AninditaInstitut Teknologi Sepuluh Nopember2
Chastine FatichahInstitut Teknologi Sepuluh Nopember1
M. Rahmat WidyantoUniversity of Indonesia1
Faosan MapaAffiliation unknown1
Riyanarto SarnoInstitut Teknologi Sepuluh Nopember1
Margo PudjiantaraInstitut Teknologi Sepuluh Nopember1
Khakim GhozaliInstitut Teknologi Sepuluh Nopember1
Tri Agung Wahyu HandrianAffiliation unknown1
Rakhmadany PrimanandaAffiliation unknown1
Baskoro Adi PratomoInstitut Teknologi Sepuluh Nopember1
Junaidi JunaidiInstitut Teknologi Sepuluh Nopember1
Prasetyo Adi WibowoInstitut Teknologi Sepuluh Nopember1
Dini YuniasriInstitut Teknologi Sepuluh Nopember1
Putri DamayantiInstitut Teknologi Sepuluh Nopember1
Waskitho WibisonoInstitut Teknologi Sepuluh Nopember1
Radityo AnggoroInstitut Teknologi Sepuluh Nopember1
Prima WiratamaInstitut Teknologi Sepuluh Nopember1
Hazna At ThooriqohInstitut Teknologi Sepuluh Nopember1
M. Naufal AzzmiInstitut Teknologi Sepuluh Nopember1
Yoga Ari TofanInstitut Teknologi Sepuluh Nopember1
Doni Putra PurbawaInstitut Teknologi Sepuluh Nopember1
Azzam Jihad UlhaqInstitut Teknologi Sepuluh Nopember1
Gusna IkhsanInstitut Teknologi Sepuluh Nopember1
Umi Laili YuhanaInstitut Teknologi Sepuluh Nopember1
Muhammad Faisal FananiInstitut Teknologi Sepuluh Nopember1
Christian Budhi SabdanaAffiliation unknown1
Noriandini Dewi SalyasariAffiliation unknown1
Izra Noor Zahara AliyaAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://juti.if.its.ac.id/index.php/juti/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc)
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">A Dual-Network iTransformer Model for Robust and Efficient Time Series Forecasting</dc:title>
	<dc:creator xml:lang="en">Shiddiqi, Ary Mazharuddin</dc:creator>
	<dc:creator xml:lang="en">Ardi, Bagaskoro Kuncoro</dc:creator>
	<dc:creator xml:lang="en">Amaliah, Bilqis</dc:creator>
	<dc:creator xml:lang="en">Mogi, I Komang Ari</dc:creator>
	<dc:creator xml:lang="en">Rizki, Agung Mustika</dc:creator>
	<dc:creator xml:lang="en">Nuralamsyah, Bintang</dc:creator>
	<dc:creator xml:lang="en">Adillion, Ilham Gurat</dc:creator>
	<dc:creator xml:lang="en">Alzamzami, Moch. Nafkhan</dc:creator>
	<dc:description xml:lang="en">Time-series forecasting plays a crucial role in various fields, including economics, healthcare, and meteorology, where accurate predictions are essential for informed decision-making. As data volume and complexity continue to grow, the need for efficient and reliable forecasting methods has become more critical. iTransformer, a recent innovation, improves interpretability while effectively handling multivariate data. In this study, the author proposes Dual-Net iTransformer, a novel approach that integrates iTransformer with a dual-network framework to enhance both accuracy and efficiency in time-series forecasting. This research aims to evaluate and compare the performance of traditional methods, iTransformer, and Dual-Net iTransformer, highlighting the advantages of the proposed model in improving forecasting outcomes.</dc:description>
	<dc:publisher xml:lang="en">Institut Teknologi Sepuluh Nopember</dc:publisher>
	<dc:date>2025-07-08</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://juti.if.its.ac.id/index.php/juti/article/view/1264</dc:identifier>
	<dc:identifier>10.12962/j24068535.v23i2.a1264</dc:identifier>
	<dc:source xml:lang="en">JUTI: Jurnal Ilmiah Teknologi Informasi; Vol.23, No.2, July 2025; 1-12</dc:source>
	<dc:source>2406-8535</dc:source>
	<dc:source>1412-6389</dc:source>
	<dc:source>10.12962/j24068535.v23i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://juti.if.its.ac.id/index.php/juti/article/view/1264/552</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Ary Mazharuddin Shiddiqi, Bagaskoro Kuncoro Ardi, Bilqis Amaliah, I Komang Ari Mogi, Agung Mustika Rizki, Bintang Nuralamsyah, Ilham Gurat Adillion, Moch. Nafkhan Alzamzami</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>