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, 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.