Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
M. Filomena Teodoro, Andrade, M. A. P., Sérgio Fernandes & Nelson Carriço (2020). Water Meters Inaccuracies Registrations: A First Approach of a Portuguese Case Study. IInternational Conference on Computational Science and its Applications - ICCSA.
Exportar Referência (IEEE) 
M. F. Teodoro et al.,  "Water Meters Inaccuracies Registrations: A First Approach of a Portuguese Case Study", in IInt. Conf. on Computational Science and its Applications - ICCSA, online, 2020
Exportar BibTeX 
@misc{teodoro2020_1777663307332,
	author = "M. Filomena Teodoro and Andrade, M. A. P. and Sérgio Fernandes and Nelson Carriço",
	title = "Water Meters Inaccuracies Registrations: A First Approach of a Portuguese Case Study",
	year = "2020",
	howpublished = "Ambos (impresso e digital)",
	url = "https://iccsa.org/iccsa-2020-goes-online"
}
Exportar RIS 
TY  - CPAPER
TI  - Water Meters Inaccuracies Registrations: A First Approach of a Portuguese Case Study
T2  - IInternational Conference on Computational Science and its Applications - ICCSA
AU  - M. Filomena Teodoro
AU  - Andrade, M. A. P.
AU  - Sérgio Fernandes
AU  - Nelson Carriço
PY  - 2020
CY  - online
UR  - https://iccsa.org/iccsa-2020-goes-online
AB  - The work described in this article results from a problem proposed by a water utility company in the framework of ESGI 140th, during June 2018. The objective is to evaluate water meters performance using historical data, knowing that, being a mechanical device, the water meters suffer a deterioration with time and use, losing accuracy throughout its cycle of use. We intend to approach a problem capable of identifying anomalies on water consumption pattern. In present work, ARIMA modeling was considered to obtain a predictive model. The results show that in the time series traditional framework revealed significant and adequate in the different estimated models. The in-sample forecast is promising, conducting to adequate measures of performance.
ER  -