Case Study in Evaluating Time Series Prediction Models Using the Relative Mean Absolute Error.
Statistical prediction models inform decision-making processes in many real-world settings. Prior to using predictions in practice, one must rigorously test and validate candidate models to ensure that the proposed predictions have sufficient accuracy to be used in practice. In this article, we pres...
| Publicado en: | American Statistician Vol. 70; no. 3; pp. 285 - 293 |
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| Autores principales: | , , , , , |
| Formato: | Case Study |
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Taylor & Francis Ltd
2016
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=117908800&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 117908800 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: 2016 vid: 70 iid: 3 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 117908800 10.1080/00031305.2016.1148631 ppf: 285 ppct: 8 formats: tig: atl: Case Study in Evaluating Time Series Prediction Models Using the Relative Mean Absolute Error. aug: au: Reich, Nicholas G. Lessler, Justin Sakrejda, Krzysztof Lauer, Stephen A. Iamsirithaworn, Sopon Cummings, Derek A. T. su: Prediction models Statistical errors Statistical models Time series analysis Dengue hemorrhagic fever Medical forecasting sug: subj: Prediction models Statistical errors Statistical models Time series analysis Dengue hemorrhagic fever Medical forecasting keyword: Accuracy Accuracy; Forecasting; Infectious disease; Prediction; Time series Forecasting Infectious disease Prediction Time series Accuracy Accuracy; Forecasting; Infectious disease; Prediction; Time series Forecasting Infectious disease Prediction Time series ab: Statistical prediction models inform decision-making processes in many real-world settings. Prior to using predictions in practice, one must rigorously test and validate candidate models to ensure that the proposed predictions have sufficient accuracy to be used in practice. In this article, we present a framework for evaluating time series predictions, which emphasizes computational simplicity and an intuitive interpretation using the relative mean absolute error metric. For a single time series, this metric enables comparisons of candidate model predictions against naïve reference models, a method that can provide useful and standardized performance benchmarks. Additionally, in applications with multiple time series, this framework facilitates comparisons of one or more models'predictive performance across different sets of data. We illustrate the use of thismetricwith a case study comparing predictions of dengue hemorrhagic fever incidence in two provinces of Thailand. This example demonstrates the utility and interpretability of the relative mean absolute error metric in practice, and underscores the practical advantages of using relative performance metrics when evaluating predictions. pubtype: Academic Journal doctype: Case Study src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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