<p>(A) Both Enright and DFT method's relative differences (<i>RD</i>) remain fairly constant on average with increasing <i>NPC</i> while recovering the fast simulated period. The Enright method returns on average, better results than the Autocorrelation or DFT methods. The Autocorrelation method is the least accurate, producing average differences starting around 60% and decreasing to 35% with 20 <i>NPC</i>. (B) The DFT method produces the most accurate results, but overall, average differences are approximately 30%. The red diamond, the green square and the blue circle represent the Autocorrelation, Enright and DFT methods, respectively.</p
A key problem in time series prediction using autoregressive models is to fix the model order, namel...
<p>A) Relationship between the number of data points and computational time for the different period...
The benchmarking problem arises when time series data for the same target variable are measured at d...
<p>(A) Both Enright and DFT method's relative differences (<i>RD</i>) slightly decrease with increas...
<p>(A) Results obtained with 0% noise for the fast period. Candidate periods with the minimum signif...
<div><p>We investigated commonly used methods (Autocorrelation, Enright, and Discrete Fourier Transf...
<p>(A) 30 minutes of oscillatory data representing Ca<sup>2+</sup> oscillations; (B) Autocorrelation...
This paper describes four methods for estimating autocorrelation time and evaluates these methods wi...
Simulations have been run to compare the performance of several period determination algorithms: ess...
<p>The figure was drawn from data in Scopus, checked in October 1st 2013. The search was limited to ...
This paper presents a computational program named BINCOR (BINned CORrelation) for estimating the co...
Notwithstanding the significant efforts to develop estimators of long-range correlations (LRC) and t...
The sample autocorrelation function is defined by the mean lagged products (LPs) of random observati...
The benchmarking problem arises when time series data for the same target variable are measured at d...
A standard final step in the DNS (but the same can be said of experimental measurements) of turbulen...
A key problem in time series prediction using autoregressive models is to fix the model order, namel...
<p>A) Relationship between the number of data points and computational time for the different period...
The benchmarking problem arises when time series data for the same target variable are measured at d...
<p>(A) Both Enright and DFT method's relative differences (<i>RD</i>) slightly decrease with increas...
<p>(A) Results obtained with 0% noise for the fast period. Candidate periods with the minimum signif...
<div><p>We investigated commonly used methods (Autocorrelation, Enright, and Discrete Fourier Transf...
<p>(A) 30 minutes of oscillatory data representing Ca<sup>2+</sup> oscillations; (B) Autocorrelation...
This paper describes four methods for estimating autocorrelation time and evaluates these methods wi...
Simulations have been run to compare the performance of several period determination algorithms: ess...
<p>The figure was drawn from data in Scopus, checked in October 1st 2013. The search was limited to ...
This paper presents a computational program named BINCOR (BINned CORrelation) for estimating the co...
Notwithstanding the significant efforts to develop estimators of long-range correlations (LRC) and t...
The sample autocorrelation function is defined by the mean lagged products (LPs) of random observati...
The benchmarking problem arises when time series data for the same target variable are measured at d...
A standard final step in the DNS (but the same can be said of experimental measurements) of turbulen...
A key problem in time series prediction using autoregressive models is to fix the model order, namel...
<p>A) Relationship between the number of data points and computational time for the different period...
The benchmarking problem arises when time series data for the same target variable are measured at d...