Trend analysis and forecast of PM2.5 in Fuzhou, China using the ARIMA model

Zhang, Lanyi; Lin, Jane; Qiu, Rongzu; Hu, Xisheng; Zhang, Huihui; Chen, Qingyao; Tan, Huamei; Lin, Danting; Wang, Jiankai

doi:10.1016/j.ecolind.2018.08.032

Cited by 237 publications

(72 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, ARMA/ARIMA is developed and also examines the performance of the model using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) (Ben Amor, Boubaker, & Belkacem, 2018;Goto & Taniguchi, 2019). As postulated by Box and Jenkins in the second half of the 1970s (Zhang et al, 2018), time series model had an autoregressive and moving average part (Gonçalves Mazzeu, Veiga, & Mariti, 2019). It means that Autoregressive (AR) and Moving Average (MA) are denoted as ARIMA (p, d, q) where p signifies the order of autoregressive process, d indicates the order of differencing of the timeseries data and qthe order of moving average process.…”

Section: Methodsmentioning

confidence: 99%

“…AR(p) model is selected when PACF spikes cut off after lag p but ACF has exponential decay to zero (Alsharif, Younes, & Kim, 2019). MA(q) model is selected if ACF cut off after lag q when PACF has an exponential decay to zero and ARMA (p, q) is selected when ACF and PACF spikes cut off at a specific p and q (Astill, Harvey, Leybourne, Sollis, & Robert Taylor, 2018;Bratu, 2012;Zhang et al, 2018). After identification is complete, and since more than one model might be feasible, diagnostic check is carried out on the estimated models and the most suitable one is selected for forecast.…”

Section: Model Specificationmentioning

confidence: 99%

See 1 more Smart Citation

Re-Evaluation of World Population Figures: Politics and Forecasting Mechanics

Shobande

Shodipe

2020

Economics and Business

View full text Add to dashboard Cite

This paper forecasts the world population using the Autoregressive Integration Moving Average (ARIMA) for estimation and projection for short-range and long-term population sizes of the world, regions and sub-regions. The study provides evidence that growth and population explosion will continue in Sub-Saharan Africa, tending the need to aggressively promote pragmatic programmes that will balance population growth and sustainable economic growth in the region. The study argued that early projections took for granted the positive and negative implications of population growth on the social structure and offset the natural process, which might have implication(s) on survival rate. Given the obvious imbalance in population growth across continents and regions of the world, a more purposeful inter-regional and economic co-operation that supports and enhances population balancing and economic expansion among nations is highly recommended. In this regard, the United Nations should compel member states to vigorously and effectively implement domestic and international support programmes with this objective in view.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Model Specificationmentioning

confidence: 99%

Re-Evaluation of World Population Figures: Politics and Forecasting Mechanics

Shobande

Shodipe

2020

Economics and Business

View full text Add to dashboard Cite

show abstract

“…No connection between every two sequential observations implies p = 0. • Constructing models and estimating its parameters [7] . • Diagnostics and selection of model – the residuals and the quality of approximation of the model are examined.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Variability analysis in PM2.5 monitoring

Bhardwaj

Pruthi

2019

Data in Brief

View full text Add to dashboard Cite

United States Environmental Protection Agency (US EPA) and Central Pollution Control Board (CPCB) are two major air quality monitoring agencies in India that measure the concentration of particulate matter of size up to 2.5 μm (PM2.5). PM2.5 study over southern Asia has significance from the environment and ecosystem viewpoint (Abdullah et al.,2007; Dockery and Stone, 2007). In order to raise alert and controlling of pollutants, not only forecasting but the accuracy of forecasting has attracted attentions from various departments of research and air quality monitoring agencies. Quest for reducing error in forecasting has never come to pause. The precursor in forecasting is data monitoring. Keeping in focus the initial phase of data analysis, PM2.5 concentration was collected from both agencies within an area of radius 3.1 miles for the year 2016. Using the data, variability analysis is carried out for the efficiency of vital environment protection agencies.

show abstract

“…The main application of the time series analysis is forecasting. There are several methods for time-series forecasting and the ARIMA method is one of the best models [13], which is based on the Box-Jenkins model [14]. In this model, univariate time series forecasting is performed by statistical modeling.…”

Section: Methodsmentioning

confidence: 99%