Abstract
Indoor Air Pollution is one of the most ignored topics that require serious investigation. People spend most of their lives in either closed AC offices or within AC bedrooms which are not monitored at all. Several indoor air pollutants can affect human health out of which \(\text {CO}_{2}\) is most dominant. Time series forecasting is a very powerful tool which has been successfully used in a wide range of research domain for predicting next moment’s value if it is time-dependent. Here, we have found this tool fits well in the present scenario and thus used for solving this issue. Out of the different time series models present in the domain, using the SARIMA model, we have achieved a prediction accuracy of 102.2 parts per million (ppm), i.e., 89.78% for the indoor pollutant prediction which is outperforming other forecasting models.
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Acknowledgements
The research work of Joy Dutta is funded by “Visvesvaraya PhD Scheme, Ministry of Electronics and IT, Government of India.” This research work is also supported by the project entitled “Participatory and Realtime Pollution Monitoring System For Smart City,” funded by Higher Education, Science and Technology and Biotechnology, Department of Science and Technology, Government of West Bengal, India.
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Dutta, J., Roy, S. (2021). Indoor Air Pollutant Prediction Using Time Series Forecasting Models. In: Hassanien, A.E., Bhattacharyya, S., Chakrabati, S., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1286. Springer, Singapore. https://doi.org/10.1007/978-981-15-9927-9_48
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DOI: https://doi.org/10.1007/978-981-15-9927-9_48
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