REDUCING SIZE USING AUTOENCODER AND FORECASTING SALES DELAYS BASED ON (Q) SVR
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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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