A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile

Díaz-Robles, Luis A.; Ortega, J.C.; Fu, Joshua S.; Reed, Gregory D.; Chow, Judith C.; Watson, John G.; Moncada-Herrera, Juan A.

doi:10.1016/j.atmosenv.2008.07.020

Cited by 336 publications

(179 citation statements)

References 47 publications

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“…This result, originally shown for ARMA methods [21], can be also applicable for other estimators [22,23].This condition usually implies a stable process [24]. This notion is directly linked to the fact that whether certain feature such as mean or variance change over time or remain constant.…”

Section: -Materials and Methodsmentioning

confidence: 89%

Statistical parameters as a means to a priori assess the accuracy of solar forecasting models

Voyant

Soubdhan

Lauret

et al. 2015

Energy

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In this paper we propose to determinate and to test a set ofstatistical parameters (20)to estimate the predictability of the global horizontal irradiation time series and thereby propose a new prospective tool indicating the expected error regardlessthe forecasting methodsa modeller can possibly implement. The mean absolute log return, which is a tool usually used in econometry, proves to be a very good estimator. Some examples of the use of this tool are exposed, showing the interest of this statistical parameter in concrete cases of predictions or optimizations.

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Section: -Materials and Methodsmentioning

confidence: 89%

Statistical parameters as a means to a priori assess the accuracy of solar forecasting models

Voyant

Soubdhan

Lauret

et al. 2015

Energy

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“…The ARIMA is a commonly used time-series forecasting methodology in environmental studies [23,24]. The general model introduced by Box and Jenkins includes autoregressive as well as moving average parameters, and implicitly contains differencing in the formulation of the model [25].…”

Section: Methodsmentioning

confidence: 99%

A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

et al. 2015

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Abstract:The metropolitan city of Istanbul is becoming overcrowded and the demand for clean water is steeply rising in the city. The use of analytical approaches has become more and more critical for forecasting the water supply and demand balance in the long run. In this research, Istanbul's water supply and demand data is collected for the period during 2006 and 2014. Then, using an autoregressive integrated moving average (ARIMA) model, the time series water supply and demand forecasting model is constructed for the period between 2015 and 2018. Three important sustainability metrics such as water loss to supply ratio, water loss to demand ratio, and water loss to residential demand ratio are also presented. The findings show that residential water demand is responsible for nearly 80% of total water use and the consumption categories including commercial, industrial, agriculture, outdoor, and others have a lower share in total water demand. The results also show that there is a considerable water loss in the water distribution system which requires significant investments on the water supply networks. Furthermore, the forecasting results indicated that pipeline projects will be critical in the near future due to expected increases in the total water demand of Istanbul. The authors suggest that sustainable management of water can be achieved by reducing the residential water use through the use of water efficient OPEN ACCESSSustainability 2015, 7 11051 technologies in households and reduction in water supply loss through investments on distribution infrastructure.

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“…Nevertheless, there are also cases in which the ARIMA models worked better [35], and the importance of selecting an appropriate ANNs architecture to solve the corresponding problem is shown [36]. As an alternative, it has been proposed to combine models deploying different methods in order to improve the time series forecasting, commonly obtaining better results than in isolated models [37]; [38]; [39]; [40]; [41]; [42]. Some of these hybrid models have been directly applied to the problem of forecasting spare-parts demand in hybrid industries, combining linear regression and ANNs and working with small sample sizes [43].…”

Section: Forecasting With the Arima And Anns Modelsmentioning

confidence: 99%

Automobile spare-parts forecasting: A comparative study of time series methods

Vargas¹,

Cortés²

2017

Int. J. Automot. Mech. Eng.

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In Mexico, the automotive industry is considered to be strategic in the industrial and economic development of the country because it generates production, employment and foreign exchange. Good demand forecasts are needed for better manufacturing management. The time series modelling tools applied to the monthly demand forecasting of automobile spare parts in Mexico are assessed, for the case of a transnational enterprise, considering affordability. The classic methods of moving averages, final value and exponential smoothing, the prestigious autoregressive integrated models of moving averages (ARIMA), the rarely implemented artificial neural networks (ANNs) and the very little explored ARIMA-ANNs hybrid models are compared. A good performance of the models involving ANNs is observed, but they were not as steady as the ARIMA models in the post-sample periods. The mean absolute percentage error (MAPE) was reduced from an original 57% to 32.65%. The obtained results could help demonstrate the importance of improving industrial forecasting methodologies for better planning.

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A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile

Cited by 336 publications

References 47 publications

Statistical parameters as a means to a priori assess the accuracy of solar forecasting models

Statistical parameters as a means to a priori assess the accuracy of solar forecasting models

A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

Automobile spare-parts forecasting: A comparative study of time series methods

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