Autoregressive models in environmental forecasting time series: a theoretical and application review

Area of Science:

• Environmental Science
• Data Science
• Public Health

Background:

• Globalization, industrialization, and urbanization impact the environment and human health.
• Understanding these impacts requires advanced analytical tools like time series analysis.
• The Autoregressive Integrated Moving Average (ARIMA) model is a widely used time series forecasting method.

Purpose of the Study:

• To review the evolutionary stages of the ARIMA model.
• To analyze the application of ARIMA and its hybrid forms in environmental and health contexts.
• To highlight the importance of ARIMA for environmental and health forecasting.

Main Methods:

• Comprehensive literature review of over 100 research papers.
• Analysis of pure ARIMA models and hybrid ARIMA modeling approaches.
• Focus on applications in environment, health, and air quality domains.

Main Results:

• ARIMA models are highly correlated with environmental studies.
• Hybrid ARIMA models show increased robustness and better pattern recognition compared to pure ARIMA.
• Combined models offer more reliable and uniform time series analysis.

Conclusions:

• Hybrid ARIMA models are essential for accurate environmental and health forecasting.
• Forecasting using ARIMA and its variants aids in proactive public health and environmental policy.
• The integration of multiple models or hybrid approaches is a standard practice for robust time series analysis.