As demonstrated in the previous section, predicting the stable temporal curve of a venue is possible and can become more accurate when temporal information from other venues or areas is selectively transferred.
More recent research has shown probabilistic predictions to be more accurate than temporal predictions.
Indeed, no correlation was found among the three tasks: Some participants were more accurate in temporal and some in spatial or numerical tasks, but there was no evidence that more accurate participants in one domain also performed better in the other domains.
Becker et al. in [6] use cellular data to characterize mobility trends across different metropolitan areas, while the authors in [7] propose using cellular data as an alternative to travel surveys so that more accurate spatio-temporal representations of mobility flows are obtained.
This suggests that the RFs with correction for stimulus correlations and asymmetries do indeed provide a more accurate description of temporal processing in retinal ganglion cells than the RFs estimated with correction for stimulus correlations only and that the explicit correction for stimulus asymmetries can be effective in the analysis of experimental responses to natural stimuli.
The blue plots display the level of relative expression (intensity ranking) among all sRNAs [ 45], and combined with the differential expression heat maps, provide a more accurate assessment of temporal patterns in differential expression and strength of expression.
Finally, the use of a new method to date truncated copies allowed us to get a more detailed appreciation of the integration and elimination processes than with complete copies alone, and, therefore, a more accurate estimate of temporal parameters of the deletion processes.
The neutral model's superiority as a null model comes not then from closer estimates of mean species abundances, or a greater mean percentage correspondence in species composition, but rather from a more accurate representation of temporal patterns of species' presence and abundance in the local community.
In comparison to the other models, ParFlow-CLM performs more accurate at predicting the temporal dynamics and the heterogeneity aggregated to catchment scale.
