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

Abstract

Though globalization, industrialization, and urbanization have escalated the economic growth of nations, these activities have played foul on the environment. Better understanding of ill effects of these activities on environment and human health and taking appropriate control measures in advance are the need of the hour. Time series analysis can be a great tool in this direction. ARIMA model is the most popular accepted time series model. It has numerous applications in various domains due its high mathematical precision, flexible nature, and greater reliable results. ARIMA and environment are highly correlated. Though there are many research papers on application of ARIMA in various fields including environment, there is no substantial work that reviews the building stages of ARIMA. In this regard, the present work attempts to present three different stages through which ARIMA was evolved. More than 100 papers are reviewed in this study to discuss the application part based on pure ARIMA and its hybrid modeling with special focus in the field of environment/health/air quality. Forecasting in this field can be a great contributor to governments and public at large in taking all the required precautionary steps in advance. After such a massive review of ARIMA and hybrid modeling involving ARIMA in the fields including or excluding environment/health/atmosphere, it can be concluded that the combined models are more robust and have higher ability to capture all the patterns of the series uniformly. Thus, combining several models or using hybrid model has emerged as a routinized custom.

Power consumption prediction for electric vehicle charging stations and forecasting income

“…The ARIMA model is an extension of the ARMA model that differs the series to make it stationary before fitting an ARMA model 35 . The difference between consecutive values of the series is calculated until it becomes stationary.…”

“…The SARIMA model is an extension of the ARIMA 35 model that is used when the series contains a seasonal component that must be modeled. The SARIMA model adds seasonal parameters that capture the seasonal patterns in the data.…”

Rice husk valorisation by in situ grown MoS2 nanoflowers: a dual-action catalyst for pollutant dye remediation and microbial decontamination

“…These results conrms that photocatalytic activity of DRH-MoS 2 through free radical scavenging activity which enhances with increased concentration. 64,65…”

Estimation of Height Changes of Continuous GNSS Stations in the Eastern Anatolia Region during the Seasonal Variation

“…𝑇 is the amount of data in the time series [47]. 𝐴𝑅 parameters should satisfy the condition for stationarity, and 𝑀𝐴 parameters should satisfy the condition for inevitability [48].…”