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The results reflect that the developed approach has better forecasting performance than other methods considered in this study.
Simulation results reveal that the proposed model has better forecasting performance than the artificial neural network model and the regression model.
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.
As shown in the RMS values presented in Table 1, ARPG has a slightly better forecasting performance than C1PG both in 2009 and 2015.
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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.
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.
In comparison with the Naive approach, the aggregation algorithms exhibit somewhat better forecasting 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.
RMSE values approaching 0 and CC values approaching 1 signify better forecast performance.
It is possible to get better information, and by analyzing that information more acutely to do a better job of forecasting performance than other people do.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com