Quantum Adaptive Time Series Prediction

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel Quantum Adaptive Time Series Prediction (QATSP) algorithm, designed to enhance prediction accuracy by dynamically adjusting model parameters based on historical data. Traditional forecasting methods rely heavily on human design, while QATSP leverages quantum self-adaptational principles to achieve superior performance through continuous optimization. The core mechanism involves a quantum-enhanced temporal evolution of a prediction model, allowing for real-time adjustments informed by observed data patterns. We demonstrate the efficacy of this approach through a series of simulations and experimental analysis, showcasing significant improvements in prediction fidelity compared to existing state-of-the-art techniques. The paper concludes with a discussion of future research directions, including exploring the scalability of the quantum adaptation process and its potential application to diverse time series datasets.

DOI: 10.5281/zenodo.22263587

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