Sentence examples for time series benchmarks from inspiring English sources

In particular, we analyze the Wind Power Prediction Tool (WPPT), which is a successfully employed model in Denmark, its generalization (GWPPT, generalized WPPT), an adaptation of the Mycielski approach, a nonparametric regression model and several univariate time series benchmarks.

In comparison with the existing state of the arts, our proposed approach not only achieves the advantage that the number of clusters can be automatically determined, but also the superior clustering performances on a range of temporal datasets, including synthetic dataset, time series benchmark, and real-world motion trajectory datasets.

A system identification and two time-series benchmark tasks are applied to demonstrate the feasibility and superiority of SIPA.

This performance is demonstrated in three different types of problems, namely, a system identification and two time-series benchmark tasks.

Thus, Friedman's DBN structure learning algorithm was used in our work and its performance in terms of reconstruction accuracy was also evaluated using synthetic gene expression datasets and a real yeast time-series benchmark dataset.

To test the dynamic memory of the network, we have applied the RRBFN in two time series prediction benchmarks (MacKey-Glass and Logistic Map).

This paper presents a novel learning methodology for multigrid-based fuzzy system (MGFS), and its application to the CATS time series prediction benchmark.

The 3D and 4D time series prediction as benchmark applications are also taken into the account for the significant contribution of the proposed work.

The learning and generalization capabilities of these neurons are verified through various three-dimensional transformations and time series predictions as benchmark problems.

They all used the IRMA network and its time series data to benchmark their methods with BANJO and TSNI [ 18, 67], as reported by Cantone et al. In Figure  4, we show first a comparison between the true IRMA network and the networks inferred by our algorithm for both the switch ON and OFF data.

Performance of the presented hybrid approach is evaluated by several benchmark time series modeling and prediction problems.