A Dual-Network iTransformer Model for Robust and Efficient Time Series Forecasting
DOI:
https://doi.org/10.12962/j24068535.v23i2.a1264Abstract
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.
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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
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