Despite proposing a quantum generative model for time series that successfully learns correlated series with multiple Brownian motions, the model has not been adapted and evaluated for financial problems. In this study, a time-series generative model was applied as a quantum generative model to actual financial data. Future data for two correlated time series were generated and compared with classical methods such as long short-term memory and vector autoregression. Furthermore, numerical experiments were performed to complete missing values. Based on the results, we evaluated the practical applications of the time-series quantum generation model. It was observed that fewer parameter values were required compared with the classical method. In addition, the quantum time-series generation model was feasible for both stationary and nonstationary data. These results suggest that several parameters can be applied to various types of time-series data.

翻译：尽管已有研究提出了针对时间序列的量子生成模型，并成功学习到包含多重布朗运动的相关序列，但该模型尚未针对金融问题进行适配与评估。本研究将时间序列生成模型作为量子生成模型应用于实际金融数据，生成了两个相关时间序列的未来数据，并与长短期记忆网络、向量自回归等经典方法进行了比较。此外，通过数值实验完成了缺失值的填补。基于实验结果，我们评估了时间序列量子生成模型的实际应用价值。研究发现，相较于经典方法，该模型所需的参数值更少。同时，量子时间序列生成模型对平稳数据与非平稳数据均具有可行性。这些结果表明，若干参数可适用于多种类型的时间序列数据。