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10.1109/ACCESS.2020.3000860 http://scihub22266oqcxt.onion/10.1109/ACCESS.2020.3000860 34192105!8043497!34192105     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+Access 2020 ; 8 (ä): 110412-110424 Nephropedia Template TP {{tp|p=34192105|t=2020. Data Modeling With Polynomial Representations and Autoregressive Time-Series Representations, and Their Connections |pdf=|usr=}}{{34192105}} gab.com Text Data Modeling With Polynomial Representations and Autoregressive Time-Series Representations, and Their Connections JO: IEEE Access http://www.kidney.de/pget.php?&tw=1&pmid=34192105 #FOAvip Two of the data modelling techniques - polynomial representation and time-series representation - are explored in this paper to establish their connections and differences. All theoretical studies are based on uniformly sampled data in the absence of noise. This paper proves that all data from an underlying polynomial model of finite degree [Formula: see text] can be represented perfectly by an autoregressive time-series model of order [Formula: see text] and a constant term [Formula: see text] as in equation (2). Furthermore, all polynomials of degree [Formula: see text] are shown to give rise to the same set of time-series coefficients of specific forms with the only possible difference being in the constant term [Formula: see text]. It is also demonstrated that time-series with either non-integer coefficients or integer coefficients not of the aforementioned specific forms represent polynomials of infinite degree. Six numerical explorations, with both generated data and real data, including the UK data and US data on the current Covid-19 incidence, are presented to support the theoretical findings. It is shown that all polynomials of degree [Formula: see text] can be represented by an all-pole filter with [Formula: see text] repeated roots (or poles) at [Formula: see text]. Theoretically, all noise-free data representable by a finite order all-pole filter, whether they come from finite degree or infinite degree polynomials, can be described exactly by a finite order AR time-series; if the values of polynomial coefficients are not of special interest in any data modelling, one may use time-series representations for data modelling. Twit Text FOAVip Data Modeling With Polynomial Representations and Autoregressive Time-Series Representations, and Their Connections ????IEEE Access http://www.kidney.de/pget.php?&tw=1&pmid=34192105 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34192105 #FOAvip English Wikipedia <ref>{{Cite pmid|34192105}}</ref> Data Modeling With Polynomial Representations and Autoregressive Time-Series Representations, and Their Connections #MMPMID34192105 Nandi AK IEEE Access 2020[]; 8 (ä): 110412-110424 PMID34192105show ga Two of the data modelling techniques - polynomial representation and time-series representation - are explored in this paper to establish their connections and differences. All theoretical studies are based on uniformly sampled data in the absence of noise. This paper proves that all data from an underlying polynomial model of finite degree [Formula: see text] can be represented perfectly by an autoregressive time-series model of order [Formula: see text] and a constant term [Formula: see text] as in equation (2). Furthermore, all polynomials of degree [Formula: see text] are shown to give rise to the same set of time-series coefficients of specific forms with the only possible difference being in the constant term [Formula: see text]. It is also demonstrated that time-series with either non-integer coefficients or integer coefficients not of the aforementioned specific forms represent polynomials of infinite degree. Six numerical explorations, with both generated data and real data, including the UK data and US data on the current Covid-19 incidence, are presented to support the theoretical findings. It is shown that all polynomials of degree [Formula: see text] can be represented by an all-pole filter with [Formula: see text] repeated roots (or poles) at [Formula: see text]. Theoretically, all noise-free data representable by a finite order all-pole filter, whether they come from finite degree or infinite degree polynomials, can be described exactly by a finite order AR time-series; if the values of polynomial coefficients are not of special interest in any data modelling, one may use time-series representations for data modelling. äData Modeling With Polynomial Representations and Autoregressive Time-(epmc).pdf DeepDyve Pubget Overpricing