PRICE AND SALES VOLUME FORECASTING USING QUANTUM SUPPORT VECTOR REGRESSION
Article Sidebar
Main Article Content
Abstract
This article analyzes the issue of accurately predicting the price and sales volume of retail products based on classical and quantum approaches. The classical Support Vector Regression (SVR) model was evaluated in comparison with the Quantum Support Vector Regression (QSVR) model based on quantum computing. Latent representations of the data were obtained through dimensionality reduction using an autoencoder and utilized for forecasting. The experimental results demonstrated that the QSVR model outperforms the classical SVR model in terms of MAE, MSE, and R2 indicators. The study effectively employed quantum kernel functions, data re-uploading feature maps, and the COBYLA optimization algorithm. The findings confirm the practical potential of quantum machine learning methods.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
1.Stühler, H., Pranjic, D., & Tutschku, C. (2024, January). Evaluating Quantum Support Vector Regression Methods for Price Forecasting Applications. Conference Paper. https://doi.org/10.5220/0012351400003636
2.Lucko, G., Vorster, M. C., and Anderson-Cook, C. M. (2007). Unknown element of owning costs – impact of residual value. Journal of Construction Engineering and Management, 133(1).
3.Chiteri, M. (2018). Cash-flow and residual value analysis for construction equipment. Master’s thesis, University of Alberta.
4.Zong, Y. (2017). Maintenance cost and residual value prediction of heavy construction equipment. Master’s thesis, University of Alberta.
5.Milošević, I., Kovačević, M., and Petronijević, P. (2021). Estimating residual value of heavy construction equipment using ensemble learning. Journal of Construction Engineering and Management, 147(7).
6.Shehadeh, A., Alshboul, O., Al Mamlook, R. E., and Hamedat, O. (2021). Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression. Automation in Construction, 129:103827.
7.Alshboul, O., Shehadeh, A., Al-Kasasbeh, M., Al Mamlook, R. E., Halalsheh, N., and Alkasasbeh, M. (2021). Deep and machine learning approaches for forecasting the residual value of heavy construction equipment: a management decision support model. Engineering, Construction and Architectural Management.
8.Stühler, H., Zöller, M., Klau, D., Beiderwellen-Bedrikow, A., and Tutschku, C. (2023). Benchmarking automated machine learning methods for price forecasting applications. In Proceedings of the 12th International Conference on Data Science, Technology and Applications – DATA, pages 30–39. INSTICC, SciTePress.
9.Zöller, M.-A., Nguyen, T.-D., and Huber, M. F. (2021). Incremental search space construction for machine learning pipeline synthesis. In Advances in Intelligent Data Analysis XIX.
10.Zoph, B. and Le, Q. V. (2016). Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578.
11.Shor, P. W. (1999). Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Review, 41(2):303–332.
12.Harrow, A. W., Hassidim, A., and Lloyd, S. (2009). Quantum algorithm for linear systems of equations. Physical Review Letters, 103(15):150502.
13.Huang, H.-Y., et al. (2021). Power of data in quantum machine learning. Nature Communications, 12(1):2631.
14.Liu, Y., Arunachalam, S., and Temme, K. (2021). A rigorous and robust quantum speed-up in supervised machine learning. Nature Physics, 17(9):1013–1017.
15.Steinwart, I. and Christmann, A. (2008). Support vector machines. Springer Science & Business Media.
16.Thanasilp, S., Wang, S., Cerezo, M., and Holmes, Z. (2022). Exponential concentration and untrainability in quantum kernel methods. arXiv preprint arXiv:2208.11060.
17.Grossi, M., Ibrahim, N., Radescu, V., Loredo, R., Voigt, K., von Altrock, C., and Rudnik, A. (2022). Mixed quantum–classical method for fraud detection with quantum feature selection. IEEE Transactions on Quantum Engineering, 3:1–12.
18.Bank, D., Koenigstein, N., and Giryes, R. (2020). Autoencoders. arXiv preprint arXiv:2003.05991.
19.Woźniak, K. A., Belis, V., Puljak, E., Barkoutsos, P., Dissertori, G., Grossi, M., Pierini, M., Reiter, F., Tavernelli, I., and Vallecorsa, S. (2023). Quantum anomaly detection in the latent space of proton collision events at the LHC. arXiv preprint arXiv:2301.10780.