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@article{179353,
        author = {Md. Abrar Khan and Dr. K. Rajitha and R. Mohan Krishna Ayyappa},
        title = {Air Quality Prediction Using Time Series Forecasting Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6163-6168},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179353},
        abstract = {Air pollution is a growing global concern with
severe implications for public health and environmental
sustainability. This study presents a real-time air quality
prediction system using time series forecasting
techniques to estimate future Air Quality Index (AQI)
levels. The system leverages machine learning models—
ARIMA, LSTM, and Facebook Prophet—trained on
historical pollutant data (e.g., PM2.5, PM10) and
meteorological variables (e.g., temperature, humidity,
wind speed). The platform integrates data preprocessing,
model evaluation using MAE and RMSE, and
visualization through a user-friendly dashboard built
with Streamlit. It also supports real-time predictions and
health advisories, aiming to enable early intervention
and informed decision-making. The project emphasizes
modularity, scalability, and accessibility for both public
and institutional use.},
        keywords = {Air Quality Index, Forecasting, IoT, Time Series, Machine Learning.},
        month = {May},
        }