Application of time-series quantum generative model to financial data
<|reference_start|>Application of time-series quantum generative model to financial data: 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.<|reference_end|>
@article{okumura2024application,
    title={Application of time-series quantum generative model to financial data},
    author={Shun Okumura, Masayuki Ohzeki, and Masaya Abe},
    journal={arXiv preprint arXiv:2405.11795},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11795},
    primaryClass={quant-ph cond-mat.dis-nn cs.LG stat.ML}
}