Sentence examples for time series behavior from inspiring English sources

However, our analysis also introduces new observations: while being in agreement with the notion of flux used in the ODE analysis in [ 10], the net-flux structures, which counteract the influence of reversible reactions, do not succeed in providing a satisfactory means for identifying the network fluxes that give rise to the time series behavior.

These different initial conditions give rise to cycles of reverse reactions that shift the simulation resources, thereby adjusting the time-series behavior.

As it can be seen by comparing the example simulation plots of this network in Figure  10 with those in Figure  5 and Figure  9, this network is closer to the complete network also in time-series behavior.

In the predictable environments (i.e., the periodic and the growing patterns) the training and the testing datasets are not complex and the ERM principle is able to capture the time-series behaviors by using smaller window sizes.

The time-series behaviors of EF in Jiangyin from 1990 to 2005, the characteristics of EF for the different urbanizing areas within the city, and the quantitative correlations between EF and urban development in Jiangyin were studied.

Time-series behaviors in the selection cycles toward larger size through a single-cell bottleneck.

Therefore, we analyzed the complete data glove time series of these behaviors and compared these to the complete time series in the toolmaking tasks.

These models are only suitable for a stationary system (Wang et al. 2013), although it has been stated that seasonal autoregressive integrated moving average (SARIMA) models can model time series that exhibit nonstationary behavior (Box et al. 2015).

This is consistent with our hypothesis that this converging structure in the phase-space representation of certain cell proliferation time series indicates a chaotic behavior.

The problem we address here is to infer the regulatory structure of a metabolic system, given a known structure for the reaction network (stoichiometry) and experimental time series for the dynamic behavior of that system.

Usually, the temporal behavior is modeled as a time series, where time explains the behavior of the relevant variable.