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QxEAI: Quantum-like evolutionary algorithm for automated probabilistic forecasting

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  • Kevin Xin
  • Lizhi Xin

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.

Suggested Citation

  • Kevin Xin & Lizhi Xin, 2024. "QxEAI: Quantum-like evolutionary algorithm for automated probabilistic forecasting," Papers 2405.03701, arXiv.org, revised Jun 2024.
  • Handle: RePEc:arx:papers:2405.03701
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    References listed on IDEAS

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    1. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    2. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
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