プレプリント / バージョン1

Quantum-like Decision Theory for Time-series Forecasting

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DOI:

https://doi.org/10.51094/jxiv.1269

キーワード:

time-series forecasting、 machine learning、 genetic programming、 quantum superposition、 natural selection、 quantum-like evolutionary algorithm

抄録

The key to forecasting is information, almost all forecasting problems are caused by incomplete information. In this paper we propose a quantum-like evolutionary algorithm for time series forecasting from an information perspective. Based on reward learning, the quantum-like evolutionary algorithm gains valuable information from time series data to produce probabilistic forecasts. The quantum-like evolutionary algorithm utilizes operation matrixes to generate a population of virtual trajectories to simulate time series data, then compute the returns of each virtual trajectory generated, and finally by means of Genetic Programming to evolve the virtual trajectory with the maximum returns that is most approximate to the observed time series data as possible. The operation matrix with maximum returns is the one utilized to produce the probabilistic forecast. By using historical data from the Dow Jones Index and Crude Oil Prices, we show that our methodology is able to produce reasonable forecasts.

利益相反に関する開示

The authors are affiliated with XINVISIONQ, INC., the developer of a commercial forecasting tool, but for transparency of the experiments conducted in this study, all data generated and produced are included in the paper's main text

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投稿日時: 2025-05-21 15:45:49 UTC

公開日時: 2025-05-28 00:21:03 UTC
研究分野
学際科学