标题： 时间序列量子生成模型在金融数据中的应用 显示英文标题

标题： Application of time-series quantum generative model to financial data

摘要： 尽管提出了一种用于时间序列的量子生成模型，该模型成功学习了具有多个布朗运动的相关序列，但该模型尚未被适应和评估用于金融问题。 在本研究中，将时间序列生成模型应用于实际金融数据作为量子生成模型。 生成了两个相关时间序列的未来数据，并与长短期记忆和向量自回归等经典方法进行了比较。 此外，进行了数值实验以填补缺失值。 基于结果，我们评估了时间序列量子生成模型的实际应用。 观察到与经典方法相比所需的参数值更少。 此外，量子时间序列生成模型适用于平稳和非平稳数据。 这些结果表明，几个参数可以应用于各种类型的时间序列数据。 显示英文摘要

摘要： 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.