Publicação
Forecasting Sales of Air Conditioner Products: A Time Series and Machine Learning Approach
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Castelli, Mauro | |
| dc.contributor.author | Varanda, Pedro Afonso Pacheco Monteiro Proença | |
| dc.date.accessioned | 2025-11-14T10:29:07Z | |
| dc.date.embargo | 2028-10-30 | |
| dc.date.issued | 2025-10-30 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics | pt_PT |
| dc.description.abstract | The timely installation of air conditioners is essential for enhancing customer satisfaction and reducing operational costs. To achieve this, the company needs an accurate forecasting model for monthly air conditioner sales volumes. This thesis explores the development of such a model by comparing time series and machine learning approaches, specifically Facebook Prophet and XGBoost, at the district level across Portugal. Following the CRISP-DM methodology, the project began by understanding the business context, followed by a time series analysis to investigate seasonal and geographical patterns. Feature engineering was performed to incorporate time-based, weather-related, and lagged sales features, which helped capture yearly trends and regional dynamics. Even without key external variables, such as promotional campaign data, the proposed models surpassed the company's existing planning approach, which relied on the previous year's values as a baseline. The results emphasize the value of district-level forecasting in facilitating more detailed operational planning and highlights the importance of continuous model improvement, as forecast accuracy could be further enhanced by including additional business and market variables. This work demonstrates the practical benefits of employing data-driven forecasting techniques, providing a scalable foundation for aligning inventory, logistics, and workforce planning more effectively with seasonal demand fluctuations. | pt_PT |
| dc.identifier.tid | 204073006 | |
| dc.identifier.uri | http://hdl.handle.net/10362/190715 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Forecasting | pt_PT |
| dc.subject | Time Series Analysis | pt_PT |
| dc.subject | Machine Learning Models | pt_PT |
| dc.subject | Business Intelligence | pt_PT |
| dc.subject | SDG 8 - Decent work and economic growth | pt_PT |
| dc.title | Forecasting Sales of Air Conditioner Products: A Time Series and Machine Learning Approach | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.embargofct | This request is made on the basis that the thesis contains confidential and proprietary data provided by a private company. To respect the company¿s data privacy policies and to protect potentially sensitive business information, I believe it is necessary to restrict public access to the full content of the thesis for a limited period. I kindly ask for the thesis to remain under embargo for a duration of three years from the date of submission. | pt_PT |
| rcaap.rights | embargoedAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Ciência de Dados e Métodos Analíticos Avançados, especialização em Business Analytics | pt_PT |