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System dynamics simulation modeling combined with statistical analyses were utilized based on monthly time-series datasets.
Throughout our experiments, we used one synthetic and two real-world water consumption time-series datasets.
ICPSR maintains and provides access to a vast archive of social science data for research and includes many large time-series datasets.
Not all profiles in a micro-array time-series datasets are mono-tone; many fluctuate.
They respectively have 38, 18, 24 and 17 proteins in the time-series datasets.
We demonstrate the applicability of DyNB by analyzing RNA-seq time-series datasets.
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Empirical analysis is conducted with nine individual models and four real-world time series datasets.
The page includes methodology, and a PDF containing the complete list of variables within the time series datasets.
Experiments performing the clustering and classification on time series datasets demonstrate that the performance of the proposed method outperforms PVQA.
Through experimental evaluation, the CBS was shown to deliver classification results with high accuracy under two real time series datasets.
We demonstrate the new method by processing two field temperature time series datasets collected using discrete temperature sensors and a high-resolution DTS profile.
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