Sentence examples for better forecast performance from inspiring English sources
RMSE values approaching 0 and CC values approaching 1 signify better forecast performance.
The experiment results clearly show that for these two particular datasets, the proposed method achieves much better forecast performance than the basic back propagation neural network and ARIMA model.
In comparison with the Naive approach, the aggregation algorithms exhibit somewhat better forecasting performance.
The results reflect that the developed approach has better forecasting performance than other methods considered in this study.
As shown in the RMS values presented in Table 1, ARPG has a slightly better forecasting performance than C1PG both in 2009 and 2015.
Simulation results reveal that the proposed model has better forecasting performance than the artificial neural network model and the regression model.
Analysis results showed that the proposed hybrid forecasting approach receive better forecasting performance compared with classical prediction models selected in this paper, and the CAEFOA obtain higher optimization efficiency than FOA.
Since it is difficult to assume that there is no correlation of activity between cells over short distances, a reasonable spatial extent should be examined for each studied region in order to provide a better forecasting performance, as discussed by many authors.
Two experiments show that: (a) the proposed method has better forecasting performance than the traditional Autoregressive Integrated Moving Average (ARIMA) model, the Persistent Random Walk Model (PRWM) and the Back Propagation (BP) neural networks; and (b) the proposed method has satisfactory performance in both of the accuracy and the time performance.
For better forecasting performance, hybrid models which combined two or more single models for communicable disease forecasting have also been explored, and previous findings indicate that hybrid models outperformed single models [ 13, 14].
In this paper, we expand on the use of this model for forecasting CARPs in Western Australia with a focus on how the standard model may be adapted to achieve better forecasting performance if anticipated changes are incorporated.
