Теоретичні та прикладні питання економіки. Збірник наукових праць.

Випуск 2 (47)

УДК 330.4:004.8

C32, C52, C53

ORCID ID 0000-0002-0197-4179

ORCID ID 0000-0003-2574-3815

DOI https://doi.org/10.17721/tppe.2023.47.11

Liashenko O., Doctor of Economics, Professor,

Taras Shevchenko National University of Kyiv, Ukraine

Yakymchuk B., PhD Student,

Taras Shevchenko National University of Kyiv, Ukraine

THE APPLICATION OF TIME-SERIES FORECASTING MODELS IN GROCERY RETAIL INDUSTRY

The grocery retail landscape in Ukraine has witnessed profound transformations, driven by disruptions like the COVID-19 pandemic and full-scale invasion, leading to unstable consumer behavior and market dynamics. In response, forecasting models must evolve to consider stochastic exogenous factors, such as blackout periods and air alarms. This study explores advanced time series forecasting models and proposes a comprehensive framework for optimal model selection.

The study introduces the Neural Prophet, a model that combines interpretability and predictive power by incorporating components like non-periodic trends, periodic seasonality, holiday effects, and regressors. The research methodology involves a comparative analysis of classical time series forecasting methods, machine learning regression approaches, and neural networks. Noteworthy models include LightGBM, RNN, TCN, and Neural Hierarchical Interpolation for Time Series (N-HiTS). Optuna hyperparameter optimization and k-fold cross-validation enhance model accuracy.

The study applies the proposed framework to forecast order quantities in the e-commerce segment of the Ukrainian grocery retail company. The system accommodates diverse factors like weather, holidays, and promotions, providing robust decision support. Anomalies are detected using the IQR method, and missing values are filled using Exponentially Weighted Moving Average. Results show the Neural Prophet consistently outperforming other models in 65% of cases, emphasizing its superiority. However, a complete transition to neural models results in reduced accuracy, highlighting the need for a nuanced approach based on data characteristics. The study presents a sophisticated framework for forecasting accuracy, supporting effective operational decision-making. Future research should explore ensemble methods while maintaining computational efficiency, aligning with the ongoing pursuit of optimized forecasting accuracy for informed decision-making in grocery retail.

Keywords: time-series forecasting, machine learning, grocery retail, artificial neural networks.

Full Text: PDF

DOI: https://doi.org/10.17721/tppe.2023.47.11