{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T23:30:10Z","timestamp":1769038210377,"version":"3.49.0"},"reference-count":31,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"vor","delay-in-days":20,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100018227","name":"National Research Foundation of Ukraine","doi-asserted-by":"publisher","award":["2021.01\/0103"],"award-info":[{"award-number":["2021.01\/0103"]}],"id":[{"id":"10.13039\/100018227","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate sales forecasting is a key factor in effective business management, especially under conditions of increasing competition and the rapid development of e\u2010commerce. Sales time series are often characterized by trends, seasonality, and random fluctuations, which complicates the selection of an appropriate forecasting method. Therefore, a comparative analysis of classical exponential smoothing models and modern hybrid approaches is highly relevant for identifying the most accurate and practically applicable sales forecasting methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>\n                      This study presents a comparative evaluation of five forecasting models\u2014Simple Exponential Smoothing (SES), Holt\u2019s model, Holt\u2013Winters\u2019 model, Theil\u2013Wage model, and SutteARIMA\u2014applied to real retail sales data. The objective is to determine which model provides the most accurate forecasts and under which conditions, thereby supporting more informed planning and inventory control. The analysis is based on the Superstore dataset (2015\u20132018), sourced from the Kaggle platform, which contains detailed e\u2010commerce sales data across three product categories: Furniture, Office Supplies, and Technology. This dataset was selected for its high quality, representative seasonal structure, and relevance to practical business forecasting scenarios. Monthly sales quantities were extracted and used to construct time series for each category. Each model was optimized for its respective parameters (e.g.,\n                      <jats:italic>\u03b1<\/jats:italic>\n                      ,\n                      <jats:italic>\u03b2<\/jats:italic>\n                      ,\n                      <jats:italic>\u03b3<\/jats:italic>\n                      for exponential smoothing and\n                      <jats:italic>\u03bb<\/jats:italic>\n                      ,\n                      <jats:italic>\u03d5<\/jats:italic>\n                      ,\n                      <jats:italic>\u03b8<\/jats:italic>\n                      for SutteARIMA) using grid search to minimize error metrics. Model performance was then evaluated using mean absolute error (MAE), mean squared error (MSE), and mean absolute percentage error (MAPE).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The results reveal that while SES, Holt\u2019s, and Theil\u2013Wage models produced moderate accuracy (MAPE up to 16%), the Holt\u2013Winters and SutteARIMA models demonstrated significantly higher performance. For example, Holt\u2013Winters achieved a MAPE of 2.88% and MAE of 5.21 in the Technology category, confirming its strength in capturing seasonal trends. SutteARIMA outperformed all models, achieving the lowest forecasting error in all categories\u2014with a MAPE of 2.64% for Technology, 3.79% for Office Supplies, and 5.34% for Furniture\u2014demonstrating excellent short\u2010term adaptability and precision. These findings underline the importance of aligning forecasting models with the specific structural characteristics of sales data, such as trend and seasonality. The study also confirms that improper parameter selection, particularly in SutteARIMA, can lead to substantial error increases, highlighting the need for careful optimization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This research provides a practical and data\u2010driven foundation for selecting appropriate sales forecasting models in retail. By integrating real\u2010world data, comparative accuracy metrics, and clear model recommendations, it supports evidence\u2010based decisions in business operations and inventory planning. The results also offer pathways for extending model application to other domains and integrating them into hybrid forecasting systems.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/acis\/6686245","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T08:58:52Z","timestamp":1768985932000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing Sales Forecasting Methods: A Comparative Evaluation of Exponential Smoothing Models and SutteARIMA"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6962-9363","authenticated-orcid":false,"given":"Nataliya","family":"Boyko","sequence":"first","affiliation":[]},{"given":"Bohdan","family":"Salabay","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"e_1_2_13_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-2070(01)00109-1"},{"key":"e_1_2_13_2_2","unstructured":"OstertagovaE.andOstertagO. 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