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Empirical analysis is conducted with nine individual models and four real-world time series datasets.
The empirical analysis in this paper involves annual and quarterly time series datasets.
Time series datasets with an interval of 0.5 min were made based on the estimated data.
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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For non-time series datasets, we chose the datasets (Ogawa, BohenSH and BohenLC) from [ 39] and [ 40].
For non-time series datasets, it is prominent that the performance of LS is the best using NRMSE.
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.
Not all profiles in a micro-array time-series datasets are mono-tone; many fluctuate.
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