An Efficient Implementation of ARIMA Technique for Air Quality Prediction
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-7610-9_32
Reference24 articles.
1. Saba A, Asghar MN (2017) Comparative analysis of machine learning techniques for predicting air quality in smart cities. IEEE. https://doi.org/10.1109/ACCESS.2019.2925082
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3. Pooja B (2019) Air quality prediction using machine learning algorithms. https://doi.org/10.7753/IJCATR0809.1006
4. Kong T, Wang Y (2017) Air quality predictive modeling based on an improved decision tree in a weather-smart grid. https://doi.org/10.1109/ACCESS
5. Weizhen L (2018) Air pollutant parameter forecasting using support vector machines. City University of Hong Kong, Hong Kong, Department of Building and Construction
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