Forecasting number of ISO 14001 certifications in the Americas using ARIMA models

Hikichi, Suzana Eda; Salgado, Eduardo Gomes; Beijo, Luiz Alberto

doi:10.1016/j.jclepro.2017.01.084

Cited by 34 publications

(15 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[2] In comparison to Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH), ARIMA produced smallest error in predicting the exchange rate of IDR against USD for the period of January 3 rd , 2000 to July 7 th , 2014. [3] Forecasting using the ARIMA method on ISO 14001 certification data in the Americas and its 13 countries between 1996 and 2015 showed that the prediction results were close to realization.…”

Section: Introductionmentioning

confidence: 86%

See 1 more Smart Citation

Optimization of ARIMA Forecasting Model using Firefly Algorithm

unggara

Musdholifah²,

Sari³

2019

Indonesian J. Comput. Cybern. Syst.

View full text Add to dashboard Cite

Time series prediction aims to control or recognize the behavior of the system based on the data in a certain period of time. One of the most widely used method in time series prediction is ARIMA (Autoregressive Integrated Moving Average). However, ARIMA has a weakness in determining the optimal model. firefly algorithm is used to optimize ARIMA model (p, d, q). by finding the smallest AIC (Akaike Information Criterion) value in determining the best ARIMA model. The data used in the study are daily stock data JCI period January 2013 until August 2016 and data of foreign tourist visits to Indonesia period January 1988 to November 2017.Based on testing, for JCI data, obtained predicted results with Box-Jenkins ARIMA model produces RMSE 49.72, whereas the prediction with the ARIMA Optimization model yielded RMSE 49.48. For the data of Foreign Tourist Visits, the predicted results with the Box-Jenkins ARIMA model resulted in RMSE 46088.9, whereas the predicted results with ARIMA optimization resulted in RMSE 44678.4. From these results it can be concluded that the optimization of ARIMA model with Firefly Algorithm produces better forecasting model than ARIMA model without Optimization.

show abstract

Section: Introductionmentioning

confidence: 86%

“…Calculating the light intensity, i.e. the AIC value, of each firefly using equation (3). To get the light intensity value, the result value from the objective function evaluation (p,d,q) is required.…”

mentioning

confidence: 99%

Optimization of ARIMA Forecasting Model using Firefly Algorithm

unggara

Musdholifah²,

Sari³

2019

Indonesian J. Comput. Cybern. Syst.

View full text Add to dashboard Cite

show abstract

“…The study of the diffusion phenomenon of MS standards is not new. Several authors have studied the diffusion of the ISO 9001 (Franceschini et al , 2004; Viadiu et al , 2006; Albuquerque et al , 2007; Chen and Liu, 2009; Cots and Casadesús, 2015; Marimon et al , 2009; Sampaio et al , 2011; Salgado et al , 2016), ISO 14001 (Albuquerque et al , 2007; Casadesús et al , 2008; Viadiu et al , 2006; Qi et al , 2011; Delmas and Montes-Sancho, 2011; Heras-Saizarbitoria et al , 2015; Hikichi et al , 2017; To and Lee, 2014), ISO/TS 16949 (Franceschini et al , 2011) and SA 8000 (Llach et al , 2015) standards. In addition, comparison of these generic MS standards with sectoral standards has been analyzed (Alonso-Almeida et al , 2013; Bernardo, Escalante, Roca and Arbussà, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated management systems diffusion models in South European countries

Cabecinhas

Domingues

Sampaio

et al. 2018

IJQRM

View full text Add to dashboard Cite

Purpose-This paper dissects the diffusion of the number of organizations that implemented multiple management systems (MSs), considering the ISO 9001, ISO 14001 and OHSAS 18001 standards (QES) in the South European countries: Italy, Portugal and Spain. In addition, based on the data collected, forecasting models were developed to assess at which extent the multiple certifications are expected to occur in each studied country. Design/methodology/approach-Data concerning the evolution of the amount of multiple MSs in Italy, Portugal and Spain were collected for the period between 1999 and 2015. The behaviour of the evolution of the number of MSs over the years was studied adopting both the Gompertz and the Logistic models. The results obtained with these two models were compared and analyzed to provide a forecast for the next years. Findings-The diffusion throughout the years of the number of MSs presents an Sshaped behaviour. The evolution of the amount of MSs in countries with a lower saturation level are properly fitted by the Gompertz model whereas the Logistic model fits more accurately when considering countries with a larger saturation level. Research limitations/implications-The data related to the early years are not available in some of the countries. To overcome this shortcoming missing data was extrapolated from the dataset provided by the annual ISO survey. Additionally, the integration level attained by each company was not assessed and, on this regard and in the scope of this paper, an integrated management system is understood as implemented when organizations have multiple MSs implemented. Practical implications-The results provide a cross-sectional portrayal of the diffusion of MSs certifications in the South European countries and enable a forecast for the trend in the next years. Originality/value-This study aims for the first time, to the best of the authors' knowledge, to analyze the diffusion of multiple MSs throughout the years.

show abstract

“…Box-Jenkins ARIMA 1 is a simulation of regressive time series models that has become widely utilized in recent years. Many kinds of Box-Jenkins ARIMA have been developed, including the ARMA approach (Rojas et al, 2008;Wang and Lu, 2006;Hwarng, 2001), the ARIMA approach (Hikichi et al, 2017;Petrevska, 2017), seasonal ARIMA (Gharbi et al, 2011;Egrioglu et al, 2009;Tsui et al, 2014), the bivariate model or ARIMA transfer (Sharma and Khare, 2001;Sun and Koch, 2001;Gröger and Rumohr, 2006), and hybrid models (Babu and Reddy, 2014;Egrioglu et al, 2009;Pong et al, 2004). Moreover, some studies have shown interest in the partitioning of the ARIMA family to improve forecasting results (Gray, 1996;Blazsek and Mendoza, 2016;Zhang, 2003;Kambouroudis et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A Competitive Model to Forecast a Stock Market Index

Nguyen-Thanh¹,

Huarng²

2020

JBAFP

View full text Add to dashboard Cite

This study proposes a competitive model using the Box–Jenkins approach to implement a Box–Jenkins ARIMA-GARCH model in order to improve financial forecasting. Differing from previous studies, we consider optimizing the lagged terms, which assist in capturing the relationships more properly. The competitive model is then used to forecast the stock market index in Taiwan. This study conducts out-of-sample forecasting and compares the root mean square errors (RMSEs) against previous studies. The results show that the competitive model outperformed in terms of both RMSEs and consistency.

show abstract

Forecasting number of ISO 14001 certifications in the Americas using ARIMA models

Cited by 34 publications

References 52 publications

Optimization of ARIMA Forecasting Model using Firefly Algorithm

Optimization of ARIMA Forecasting Model using Firefly Algorithm

Integrated management systems diffusion models in South European countries

A Competitive Model to Forecast a Stock Market Index

Contact Info

Product

Resources

About