• Dixita Dessai
• Shaba Dessai
• Sufola Das Chagas Silva e Araujo

This study conducts a Time Series Analysis of air quality indicators with an aim to forecast the values of air pollutant concentrations like SO2, NO2 and PM10, using Auto Regressive Integrated Moving Average (ARIMA) and Long Short Term Memory (LSTM), for the cities of Margao and Sanguem in the Indian state of Goa. On comparing the results from the two methods, based on the performance measures of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) it is observed that LSTM model achieves a fair prediction of the air pollutant concentration values.

Time Series Analysis, Forecasting, ARIMA, LSTM, MAPE, RMSE, Air Quality Index (AQI).

"Time Series Analysis for Air Quality Forecasting", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 7, page no.272-277, July-2020, Available :http://www.jetir.org/papers/JETIR2007331.pdf

"Time Series Analysis for Air Quality Forecasting", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 7, page no. pp272-277, July-2020, Available at : http://www.jetir.org/papers/JETIR2007331.pdf