Prediction of the toilet’s health and manpower deployment orchestration
Predictive Analysis is widely used today: From weather forecasts to pandemic modelling to prediction of stock market trends. There are many approaches in Predictive Analysis, and every single set of data uses a variety of different approaches to model and predict future trends. This project aims to...
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| 格式: | Final Year Project |
| 語言: | English |
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Nanyang Technological University
2020
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/139963 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | Predictive Analysis is widely used today: From weather forecasts to pandemic modelling to prediction of stock market trends. There are many approaches in Predictive Analysis, and every single set of data uses a variety of different approaches to model and predict future trends. This project aims to employ Predictive Analysis to optimise cleaners’ cleaning schedules in Best Mall by creating a mathematical model. The collected data will be processed and analysed, using various techniques such as correlation matrix and Factor Analysis to create a Cleanliness Index as an indication of cleanliness in the toilets of Best Mall. The models used in this project are the ARIMA model and the TBATS model. Results suggest that cleanliness, being such a complex and intangible variable, may require a more complex model. The ARIMA model, being a simple model, was not a suitable model for the data. But the TBATS model, incorporating most aspects of ARIMA in it, may be superior to ARIMA. |
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