Automatic identification of ARIMA models: The case of water consumption forecasting

被引:0
作者
Gandara, Africa Ruiz
Caridad, Daniel
Caridad, Jose Maria
机构
来源
STRATEGIC MANAGEMENT AND ITS SUPPORT BY INFORMATION SYSTEMS, 10TH INTERNATIONAL CONFERENCE, 2013 | 2013年
关键词
Automatic identification; Arima models; predictive criteria; water consumption; ARTIFICIAL NEURAL-NETWORKS; TIME-SERIES; INFORMATION CRITERION; DERIVATION; SELECTION;
D O I
暂无
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
Selecting an ARIMA model is addressed using the usual goodness of fit measures, and, also, with some proposed predictive criteria. Both methods are compared with the aim of obtaining optimum forecasts. Several statistics such as BIC and others, obtained within the sample range, are used with simulated data, and some smoothing predictive criteria are compared as a tool for automatic selection of a 'real' model. Classical methods identify the correct model barely in a third of the cases, as it is show with simulated series, and, very often a different model produces better forecasts. A case study with water consumption in urban areas is presented with the comparative results attained with both approaches.
引用
收藏
页码:183 / 194
页数:12
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