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komendantova-et-al-2025-can-short-term-memory-processes-be-accurately-detected-a-reexamination-of-existing-definitions.pdf

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/109405 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/109405 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/109405 archive 353 disk0/00/02/04/97 2025-04-02 08:06:41 2025-12-03 10:46:16 2025-04-02 08:06:41 article show Hassani H. 9469 Royer-Carenzi M. Yeganegi R. 2456 0000-0003-4109-0690 Mashhad L.M. Komendantova N. 1880 0000-0003-2568-6179 pub ASA CAT Short-Term memory process, sum of sample autocorrelation function, Hassani's -1/2 theoremspectral density, time series, autocorrelation One major inadequacy in using the sample autocorrelation function (ACF) is the results from sample properties. Hassani’s [Formula: see text] theorem demonstrates that the sum of the sample ACF is always [Formula: see text] for any time series with any length. This result has led to doubts about methodologies that sum sample ACFs for diagnostics and analyses. Thus, the current tools and approaches fall short in detecting short-memory processes with due accuracy. Perhaps the larger question that looms here is about whether, with such definitions and methods, short-memory processes can really be picked up? Resolving this issue stands as a basic precursor to strong predictions and to precluding model mis-specification. 2025-03-25 published World Scientific doi:10.1142/S1793962325500345 3410 3692 161 public International Journal of Modeling, Simulation, and Scientific Computing 16 e02 TRUE 1793-9623 no info:eu-repo/semantics/article