@inproceedings{526a24e3e3a449fc82e0a00c0e397263,
title = "Predicting Demand in IoT Enabled Service Stations",
abstract = "The current world of AI revolves around forecasting and prediction, eating up a major chunk of problem statements generated by organizations in various domains. Applying forecasting techniques to get an advance view of the number of visitors per bay in a service station will help the owners to plan and adjust their resources for increased operational efficiency as well as to manage any sudden change in visitor demand. As part of this paper, we create a prediction model for forecasting expected visitors demand for a service station. We compare the results of applying various machine learning and statistical methods using historical visitor data as well as historical weather data. The result states that, among the techniques used, neural network using both historical and weather data performs best, providing a future view of expected demand with high accuracy.",
keywords = "arima, elastic net, kneighbors regressor, moving average, neural network, svr",
author = "Khargharia, {Himadri Sikhar} and Siddhartha Shakya and Russell Ainslie and Sara AlShizawi and Gilbert Owusu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2019 ; Conference date: 08-04-2019 Through 11-04-2019",
year = "2019",
month = apr,
doi = "10.1109/COGSIMA.2019.8724239",
language = "British English",
series = "Proceedings - 2019 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "81--87",
editor = "Rogova, {Galina L.} and Nicolette McGeorge and Gundersen, {Odd Erik} and Kellyn Rein and Mary Freiman",
booktitle = "Proceedings - 2019 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2019",
address = "United States",
}