High-Dimensional Time Series Forecasting Models: A Quantum Adaptive Learning Approach

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the application of quantum self-adaptive learning to high-dimensional time series forecasting models, leveraging the unique properties of quantum computing to enhance prediction accuracy and efficiency, particularly in scenarios involving large datasets and complex feature representations. We propose a novel "Quantum Adaptive Adjustment" mechanism that dynamically adjusts the learning process based on the characteristics of the time series data, leading to improved model performance. The research focuses on mitigating the limitations of traditional machine learning algorithms in handling high dimensionality and complex patterns, presenting a promising approach for improved forecasting capabilities. The paper details the implementation of the quantum self-adaptive learning process, including the introduction of the adaptive adjustment mechanism and a comprehensive evaluation of its effectiveness through simulations and a benchmark dataset. We conclude with a discussion of the potential impact of this approach on various real-world time series forecasting tasks.

DOI: 10.5281/zenodo.22203381

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