Hybrid demand forecasting models: pre-pandemic and pandemic use studies

Abstract

Research background: In business practice and academic sphere, the question of which of the prognostic models is the most accurate is constantly present. The accuracy of models based on artificial intelligence and statistical models has long been discussed. By combining the advantages of both groups, hybrid models have emerged. These models show high accuracy. Moreover, the question remains whether data in a dynamically changing economy (for example, in a pandemic period) have changed the possibilities of using these models. The changing economy will continue to be an important element in demand forecasting in the years to come. In business, where the concept of just in time already proves to be insufficient, it is necessary to open new research questions in the field of demand forecasting. Purpose of the article: The aim of the article is to apply hybrid models to bicycle sales e-shop data with a comparison of accuracy models in the pre-pandemic period and in the pandemic period. The paper examines the hypothesis that the pandemic period has changed the accuracy of hybrid models in comparison with statistical models and models based on artificial neural networks. Models: In this study, hybrid models will be used, namely the Theta model and the new forecastHybrid, compared to the statistical models ETS, ARIMA, and models based on artificial neural networks. They will be applied to the data of the e-shop with the cycle assortment in the period from 1.1. 2019 to 5.10 2021. Whereas the period will be divided into two parts, pre-pandemic, i.e. until 1 March 2020 and pandemic after that date. The accuracy evaluation will be based on the RMSE, MAE, and ACF1 indicators. Findings & value added: In this study, we have concluded that the prediction of the Hybrid model was the most accurate in both periods. The study can thus provide a scientific basis for any other dynamic changes that may occur in demand forecasting in the future. In other periods when there will be volatile demand, it is essential to choose models in which accuracy will decrease the least. Therefore, this study provides guidance for the use of methods in future periods as well. The stated results are likely to be valid even in an international comparison.

Determination of iron procurement strategy for manufacturing companies

“…There are several methods for price forecasting, with some of the most appropriate methods being those that examine time series variables in a non-linear and dynamic way closer to reality, i.e. iron price fluctuations (Lv et al, 2022;Kolková, & Ključnikov, 2021;Kolkova & Rozehnal, 2022). A time series includes statistical data on various quantitative indicators of economic and social phenomena in a time sequence, and methods based on time series analysis comprise various estimation techniques (Landmesser, 2021;Fiszeder & Małecka, 2022), where the most important technique is the exponential smoothing estimation method (Kahraman & Akay, 2022).…”

Bicycle Sales Prediction Based on Ensemble Learning

“…Reviewing the existing literature, it is evident that there is a lack of comprehensive analysis and accurate predictions bicycle sales forecasting, caused by variations in sample data, research scope, and methodologies employed [5][6][7][8][9]. In light of this research gap, this study aims to examine the factors influencing bicycle sales and Utilize ensemble learning to forecast and analyze the sales.…”