  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.
