Time series modeling is a commonly used approach in exchange for studying and analyzing the data to support decision-making in companies based on historical data and thereby help them to save costs. This work introduces a forecasting framework that utilizes a seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model to forecast the number of people expected to enter a building within a short period. We applied the model to forecast the abovementioned value at California University Irvine's main door using an open-source dataset that comprised data spanning four months. The experimental results demonstrate that the SARIMAX model exhibits encouraging performance in classification andevaluation, as evidenced by the promising results. The RMSE values for one,two, three, and four prediction weeks are 24.6, 40.4, 36, and 38.7, respectively, accompanied by corresponding percentage errors of 2%, 4.8%,4.76%, and 1.01%. These metrics highlight the model's ability to predict outcomes accurately and indicate its effectiveness in forecasting over various time horizons. Furthermore, the proposed model addresses the issue of inadequate future planning and analyzes foot traffic to provide a reliable forecasting technique, which is essential for modern building facilities management.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hh-50892 |
| Date | January 2023 |
| Creators | Albashir, Nour Alhuda, Danial, Hamoud |
| Publisher | Högskolan i Halmstad, Akademin för informationsteknologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.002 seconds