Automatic ARIMA modeling including interventions, using time series expert software

Mélard, Guy; Pasteels, Jean-Michel

doi:10.1016/s0169-2070(00)00067-4

Cited by 65 publications

(32 citation statements)

References 19 publications

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“…Liu (1989) proposed a method for identification of seasonal ARIMA models using a filtering method and certain heuristic rules; this algorithm is used in the SCA-Expert software. Another approach is described by Mélard and Pasteels (2000) whose algorithm for univariate ARIMA models also allows intervention analysis. It is implemented in the software package "Time Series Expert" (TSE-AX).…”

Section: Arima Modelsmentioning

confidence: 99%

Automatic Time Series Forecasting: TheforecastPackage forR

Hyndman¹,

Khandakar²

2008

J. Stat. Soft.

2,714

2,062

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Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algorithms are applicable to both seasonal and non-seasonal data, and are compared and illustrated using four real time series. We also briefly describe some of the other functionality available in the forecast package.

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Section: Arima Modelsmentioning

confidence: 99%

Automatic Time Series Forecasting: TheforecastPackage forR

Hyndman¹,

Khandakar²

2008

J. Stat. Soft.

2,714

2,062

View full text Add to dashboard Cite

show abstract

“…Although this technique has been extensively used for a time series forecasting, ARIMA model requires instruction in statistical analysis as well as appropriate knowledge of the field of application. Furthermore, the accessibility of an easy to use but adjustable specialized computer program is essential [26]. Therefore, one of the software which is used for developing ARIMA forecasting model is the R statistical software package.…”

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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“…Liu [9] proposed an algorithm that is used in the SCA-Expert software for identification of seasonal ARIMA models using a filtering method. Melard and Pasteels [10] develops another approach for univariate ARIMA models that allows intervention analysis. The name of the software package where their method is implemented is called "Time Series Expert" (TSE-AX).…”

Section: Arimamentioning

confidence: 99%

Educational Data Mining on Performance of Under Graduate Students of Dibrugarh University using R

Hussain¹,

Hazarika²,

Hazarika³

et al. 2015

IJCA

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Most of the Universities and Educational Institutions of repute adopt educational data mining for the improvement of quality in education in general and students' improvement in particular. The research was carried out with the data of the students enrolled in different affiliated colleges of Dibrugarh University. The study explores the effect of performance on the basis of gender and caste. Another analysis was carried out to examine the trends of performance with respect to time using ARIMA Model. The authors also investigated impact of some of the socio-demographic factors on the performance of the students.

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Automatic ARIMA modeling including interventions, using time series expert software

Cited by 65 publications

References 19 publications

Automatic Time Series Forecasting: TheforecastPackage forR

Automatic Time Series Forecasting: TheforecastPackage forR

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

Educational Data Mining on Performance of Under Graduate Students of Dibrugarh University using R

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