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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi
I Komang Ari Mogi
Institut Teknologi Sepuluh Nopember
Computer Science & IT Dentistry Education Library & Information Science

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A Dual-Network iTransformer Model for Robust and Efficient Time Series Forecasting Ary Mazharuddin Shiddiqi; Bagaskoro Kuncoro Ardi; Bilqis Amaliah; I Komang Ari Mogi; Agung Mustika Rizki; Bintang Nuralamsyah; Ilham Gurat Adillion; Moch. Nafkhan Alzamzami
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1264

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.
Co-Authors Agung Mustika Rizki Ary Mazharuddin Shiddiqi Bagaskoro Kuncoro Ardi Bilqis Amaliah Bintang Nuralamsyah Ilham Gurat Adillion Moch. Nafkhan Alzamzami