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

• Type: Article in International Conference Proceedings Book
• Date: 2024-06-25

Authors

Da Costa, JP (Author) FCUP

Conference proceedings International

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

Pages: 1327-1332

IEEE 22nd Mediterranean Electrotechnical Conference (MELECON)

Porto, PORTUGAL, JUN 25-27, 2024

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ISI Web of Knowledge - 0 Citations

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Scientific classification

CORDIS: Technological sciences

FOS: Natural sciences

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6