REDUCING SIZE USING AUTOENCODER AND FORECASTING SALES DELAYS BASED ON (Q) SVR
DOI:
https://doi.org/10.55640/Keywords:
autoencoder, SVR, QSVR, latent representation, size compression, regression, price forecast, MAE, RMSE, R2.Abstract
This article examines the issue of identifying and forecasting delays in the price and sales volume of retail products. In the proposed approach, size reduction using an Autoencoder and compressed latent representations are used in the Support Vector Regression (SVR) and Quantum Support Vector Regression (QSVR) models. Data on 51 types of products were cleared and enriched based on time factors and categorical attributes over a 15-month period. Latent layers were transmitted as inputs to regression models, and the results confirmed that the QSVR model showed higher accuracy and lower error compared to SVR. The findings show the practical effectiveness of the Autoencoder → (Q) SVR pipeline in predicting delays.
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References
1.Zong, C., et al. (2017). Residual value prediction of articulated trucks using regression models. Journal of Transportation Engineering.
2.Chiteri, A. (2018). Auction-based valuation of used trucks using resale data. Applied Economics.
3.Milošević, D. (2021). Ensemble regression models for price prediction of construction equipment. International Journal of Forecasting.
4.Shehadeh, H., & Alshboul, M. (2021). Predicting residual value of construction machinery using regression analysis. Journal of Construction Engineering and Management.
5.Stühler, T., et al. (2023). Comparative analysis of AutoML and ML models for online retail equipment sales forecasting. Machine Learning Applications.
6.Schuld, M., Sinayskiy, I., & Petruccione, F. (2015). An introduction to quantum machine learning. Contemporary Physics.
7.Havlíček, V., et al. (2019). Supervised learning with quantum-enhanced feature spaces. Nature.
8.Biamonte, J., et al. (2017). Quantum machine learning. Nature.
9.Goodfellow, I., et al. (2016). Deep Learning. MIT Press.
10.Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer.
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