Application of time-series quantum generative model to financial data
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917670732955648 |
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| author | Okumura, Shun Ohzeki, Masayuki Abe, Masaya |
| author_facet | Okumura, Shun Ohzeki, Masayuki Abe, Masaya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11795 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Application of time-series quantum generative model to financial data Okumura, Shun Ohzeki, Masayuki Abe, Masaya Quantum Physics Disordered Systems and Neural Networks Machine Learning 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. |
| title | Application of time-series quantum generative model to financial data |
| topic | Quantum Physics Disordered Systems and Neural Networks Machine Learning |
| url | https://arxiv.org/abs/2405.11795 |