Forecasts using Box–Jenkins models for the ambient air quality data of Delhi City

Sharma, Prerana; Chandra, Avinash; Kaushik, S.C.

doi:10.1007/s10661-008-0520-2

Cited by 22 publications

(8 citation statements)

References 10 publications

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“…The forecasting results acquired in the present study (Table 1) are compared with the results of Sharma et al, (2009) study who applied an identical approach for developing ARMA/ ARIMA models and for determining the statistical efficiency to forecasts the ambient air quality of Delhi City. Our studies obtain at least 89% of forecasting free from error for all of the models in comparison to at least 86.93% forecasting accuracy of (Sharma et al 2009) for all the models.…”

Section: Resultsmentioning

confidence: 99%

“…Liu, (2009) forecasted day by day aggregation level using Box-Jenkins time series models and multivariate analysis. Numerous univariate ARMA/ARIMA models were developed by Sharma et al, (2009) for assessing and predicting a monthly maximum of the 24-hours average time series data for sulphur dioxide, nitrogen oxide and suspended particulate matter aggregation in an urban region of Delhi city. Kumar and Jain, (2010) developed univariate ARIMA models for predicting the daily mean of ambient air pollutant such as ozone, carbon monoxide, nitric oxide and nitrogen dioxide aggregation at an urban traffic location.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing and forecasting ambient air quality of Chennai city in India

Nadeem

Ilyas

Uduman

2020

GES

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Regardless of the existing governmental and public preventive actions for surveillance and controlling the air quality in several regions of the Chennai city in India, the air quality does not meet the desired standard. In this regard, this study employs an ARMA/ARIMA modelling approach for forecasting Respirable Suspended Particulate Matter (RSPM), Sulphur dioxide (SO 2) and Nitrogen dioxide (NO 2) concentration for three most polluted sites in Chennai city. A total of nine univariate linear stochastic models have been developed, three for each of the stations which includes one for each of the specific pollutants in order to forecasts the concentration of each pollutant. The evaluation of the model statistics R 2 values and index of agreement values evince that a significant level of real-time forecasting of the pollutants can be achieved by employing the developed ARMA/ARIMA models. Moreover, the comparisons of actual air pollutant concentration have been carried out with the permissible limit as prescribed by the National ambient air quality standards (NAAQS) of India for assessing the level of pollution of all three locations.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing and forecasting ambient air quality of Chennai city in India

Nadeem

Ilyas

Uduman

2020

GES

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“…the Holt-Winters methods. For instance, the Box-Jenkins ARIMA model, is commonly used in fitting forecasting models when dealing with a non-stationary time series, and this model has been used extensively in health forecasting [27,33,[52][53][54][55].…”

Section: Time Series and Health Forecastingmentioning

confidence: 99%

An overview of health forecasting

Soyiri

Reidpath

2012

Environ Health Prev Med

123

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Health forecasting is a novel area of forecasting, and a valuable tool for predicting future health events or situations such as demands for health services and healthcare needs. It facilitates preventive medicine and health care intervention strategies, by pre-informing health service providers to take appropriate mitigating actions to minimize risks and manage demand. Health forecasting requires reliable data, information and appropriate analytical tools for the prediction of specific health conditions or situations. There is no single approach to health forecasting, and so various methods have often been adopted to forecast aggregate or specific health conditions. Meanwhile, there are no defined health forecasting horizons (time frames) to match the choices of health forecasting methods/approaches that are often applied. The key principles of health forecasting have not also been adequately described to guide the process. This paper provides a brief introduction and theoretical analysis of health forecasting. It describes the key issues that are important for health forecasting, including: definitions, principles of health forecasting, and the properties of health data, which influence the choices of health forecasting methods. Other matters related to the value of health forecasting, and the general challenges associated with developing and using health forecasting services are discussed. This overview is a stimulus for further discussions on standardizing health forecasting approaches and methods that will facilitate health care and health services delivery.

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“…A aplicabilidade dos modelos de séries temporais pode ser vista nas mais diversas áreas de estudo (Tablada et al 2016): na saúde (Bicalho et al 2014), na meteorologia (Liska et al 2013), nas ciências econômicas, entre outras, em virtude da dependência temporal apresentada pela maioria das variáveis (Barbosa et al 2015). Uma das classes de modelos de séries temporais mais difundidos é a abordagem proposta por Box e Jenkins (Box e Jenkins 2015), que consiste em decompor a série temporal em componentes autorregressivos de médias móveis (Sharma et al 2009), com o objetivo de analisar o comportamento, as tendências e as correlações dos dados observados (Oliveira et al 2015) e, a partir desta análise, obter estimativas viáveis e confiáveis para o fenômeno em estudo. Serra (2013) realizou estudos de econometria em séries temporais analisando as interações de volatilidade entre os mercados de biocombustíveis e combustíveis fósseis.…”

Section: Introductionunclassified

Modelagem E Previsão Da Produção Brasileira De Biodiesel Utilizando Séries Temporais

Xavier

Barbosa

Júnior

et al. 2018

G. Sci.

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Os biocombustíveis são, atualmente, as melhores alternativas para a redução do uso de combustíveis fósseis, que representam a maior parte da matriz energética brasileira. Oriundo principalmente a partir de biomassa vegetal, o biodiesel é um combustível limpo e renovável que pode substituir quase todos os derivados de petróleo sem modificações nos motores, diminuindo a dependência dos combustíveis fósseis e causando menores prejuízos ao meio ambiente, se comparado aos prejuízos ocasionados por combustíveis derivados do petróleo. Com base na preocupação ambiental e através de análises de séries temporais, foram propostos possíveis modelos de predição para a produção de brasileira de biodiesel, utilizando dados da produção nacional em m³ no período entre janeiro de 2005 e dezembro de 2015, disponibilizados livremente através do site da ANP - Agência Nacional do Petróleo, Gás Natural e Biocombustíveis. Os modelos de predição foram estimados considerando a sazonalidade apresentada pela série temporal, através da metodologia de Box e Jenkins e do algoritmo de alisamento exponencial de Holt-Winters. Os modelos propostos responderam satisfatoriamente às expectativas, mostrando erros de predição relativamente pequenos, bem como um bom ajuste aos dados. O trabalho apresentou resultados que contribuem positivamente para a sociedade, na busca de recursos cada vez mais ecologicamente sustentáveis.

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Forecasts using Box–Jenkins models for the ambient air quality data of Delhi City

Cited by 22 publications

References 10 publications

Analyzing and forecasting ambient air quality of Chennai city in India

Analyzing and forecasting ambient air quality of Chennai city in India

An overview of health forecasting

Modelagem E Previsão Da Produção Brasileira De Biodiesel Utilizando Séries Temporais

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