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QxEAI: Quantum-like evolutionary algorithm for automated probabilistic forecasting

Published 2 May 2024 in physics.soc-ph, cs.AI, cs.LG, cs.NE, econ.GN, and q-fin.EC | (2405.03701v2)

Abstract: Forecasting, to estimate future events, is crucial for business and decision-making. This paper proposes QxEAI, a methodology that produces a probabilistic forecast that utilizes a quantum-like evolutionary algorithm based on training a quantum-like logic decision tree and a classical value tree on a small number of related time series. We demonstrate how the application of our quantum-like evolutionary algorithm to forecasting can overcome the challenges faced by classical and other machine learning approaches. By using three real-world datasets (Dow Jones Index, retail sales, gas consumption), we show how our methodology produces accurate forecasts while requiring little to none manual work.

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