The ACF defines the correlation between series of data at different times.
ANN are statistical mathematical tools able to determine the existence of a correlation between series of data and, once 'trained', to predict output data given input data.
First, the time frequency phenomena of the analysis results from one-to-one and many-to-one correlation time series support the hypothesis of the regional and global characteristics of the oil market, respectively.
We assume one-step first-order correlation time series structure so that HMM contains with Poisson distributed state-dependent distribution.
To test whether the R2 values were the result of random correlations, a series of permutation tests were performed for each oxidation ratio and repeated 3 times for each ratio.
Empirical understanding of attribute-pressure relationships based on spatial correlations, time series, and experiments (e.g., [5], [8], [22]), are valuable sources of this information.
Other comparative approaches have been used or suggested (especially to compare timeliness of data sources), including cross-correlation time series modeling [23], [24], comparison of peaks [22], and comparison of aberration detection [26].
The models also contained first-order autoregressive serially correlated errors to account for time series correlation within each dog exposure.
The correlations are series-specific, and different correlations for the same CYP are not uncommon.
ARFIMA/ARMA modeling detected long-range correlation in 16 series over 18 in synchronization, and in 17 series over 18 in syncopation.
