White Noise and Its Misapplications: Impacts on Time Series Model Adequacy and Forecasting

20387 7 108836 2 329739 document 108836 forecasting-07-00008.pdf application/pdf 3993a760c8d4f296f1a92cab735bd609 MD5 3123413 2025-02-06 07:19:30 https://pure.iiasa.ac.at/id/eprint/20387/1/forecasting-07-00008.pdf 20387 1 1 application/pdf text en public cc_by_4

forecasting-07-00008.pdf

published 108837 2 329745 document 108837 lightbox.jpg image/png 26c5e1d4412a1ad81930c377a9cd12f0 MD5 52945 2025-02-06 07:29:34 https://pure.iiasa.ac.at/id/eprint/20387/2/lightbox.jpg 20387 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/108836 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/108836 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/108836 archive 353 disk0/00/02/03/87 2025-02-06 07:22:13 2025-03-12 05:01:17 2025-02-06 07:22:13 article show Hassani H. 9469 Mashhad L.M. Royer-Carenzi M. Yeganegi R. 2456 0000-0003-4109-0690 Komendantova N. 1880 0000-0003-2568-6179 White Noise and Its Misapplications: Impacts on Time Series Model Adequacy and Forecasting pub ASA CAT time series analysis; model selection; Hassani −1/2 theorem; white noise; ARMA; Gaussian; Ljung–Box test This paper contributes significantly to time series analysis by discussing the empirical properties of white noise and their implications for model selection. This paper illustrates the ways in which the standard assumptions about white noise typically fail in practice, with a special emphasis on striking differences in sample ACF and PACF. Such findings prove particularly important when assessing model adequacy and discerning between residuals of different models, especially ARMA processes. This study addresses issues involving testing procedures, for instance, the Ljung–Box test, to select the correct time series model determined in the review. With the improvement in understanding the features of white noise, this work enhances the accuracy of modeling diagnostics toward real forecasting practice, which gives it applied value in time series analysis and signal processing. 2025-02-05 published MDPI doi:10.3390/forecast7010008 3410 3692 161 public Forecasting 7 1 8 TRUE 2571-9394 no info:eu-repo/semantics/article