QxEAI: Quantum-like evolutionary algorithm for automated probabilistic forecasting

TL;DR

This paper introduces QxEAI, a quantum-inspired evolutionary algorithm that leverages quantum-like logic decision trees to generate accurate probabilistic forecasts from limited data, outperforming classical methods.

Contribution

The paper presents a novel quantum-like evolutionary algorithm for probabilistic forecasting that effectively handles small datasets and reduces manual intervention.

Findings

Accurately forecasts Dow Jones Index, retail sales, and gas consumption.

Requires minimal manual tuning and small data samples.

Outperforms traditional forecasting methods in tested datasets.

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.