Optimization strategies in SEI: An analysis of SARIMA and additive Holt-Winters models

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024-06-25

Autores

Da Costa, JP (Autor) FCUP

Ata de Conferência Internacional

Título: 2024 IEEE 22ND MEDITERRANEAN ELECTROTECHNICAL CONFERENCE, MELECON 2024

Páginas: 1327-1332

IEEE 22nd Mediterranean Electrotechnical Conference (MELECON)

Porto, PORTUGAL, JUN 25-27, 2024

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Classificação Científica

CORDIS: Ciências Tecnológicas

FOS: Ciências exactas e naturais

Outras Informações

ID Authenticus: P-016-W3N

DOI: 10.1109/melecon56669.2024.10608496

Abstract (EN): This paper focuses on the importance of Business Intelligence (BI) tools in the business context and the urgent need for more effective implementation of time series forecasting models in these resources. It shows the utility and applicability of Sage Enterprise Intelligence (SEI), an integrated BI tool in Enterprise Resource Planning (ERP) Sage, by illustrating how it enhances data analysis and decision-making processes. Additionally, a study will show the application of time series forecasting models: Seasonal AutoRegressive Integrated Moving Average (SARIMA) and additive Holt-Winters to the sales value of a fuel sector company. The research was conducted through a case study in which sales data were collected from 2016 to 2023. The results indicate that neither of the two models exceeded the sales figures reflecting the company's market position. In this case study, both models performed well, with the residuals verifying the assumptions. However, the additive Holt-Winters model had lower errors, which is why it was selected for the final step: forecasting 12 months.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6