QxEAI: Quantum-like evolutionary algorithm for automated probabilistic forecasting
This addresses forecasting challenges for business and decision-making, but appears incremental as it builds on existing evolutionary and quantum-like methods.
The paper tackles the problem of probabilistic forecasting by proposing QxEAI, a quantum-like evolutionary algorithm that trains on small time series datasets, and demonstrates accurate results on real-world datasets like the Dow Jones Index, retail sales, and gas consumption with minimal manual effort.
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.