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

Ary Mazharuddin Shiddiqi (Institut Teknologi Sepuluh Nopember)
Bagaskoro Kuncoro Ardi (Institut Teknologi Sepuluh Nopember)
Bilqis Amaliah (Institut Teknologi Sepuluh Nopember)
I Komang Ari Mogi (Institut Teknologi Sepuluh Nopember)
Agung Mustika Rizki (Institut Teknologi Sepuluh Nopember)
Bintang Nuralamsyah (Institut Teknologi Sepuluh Nopember)
Ilham Gurat Adillion (Institut Teknologi Sepuluh Nopember)
Moch. Nafkhan Alzamzami (Institut Teknologi Sepuluh Nopember)

JUTI: Jurnal Ilmiah Teknologi Informasi
Vol.23, No.2, July 2025

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.