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Unfortunately, however, it is not always easy to investigate whether such a declustering-based RI algorithm has poorer or better forecast accuracy.
Results from the study show that the Reg-SARIMA GARCH model pReg-SARIMA GARCHorecast accuracy with a modelabsolute produceserror (MAPE) of 1.42%.
Elsharkawy (1998) showed that radial basis function type neural network model had better forecast accuracy than the conventional methods in terms of predicting the oil formation volume factor, solution gas oil ratio, oil viscosity, saturated oil density, under-saturated oil compressibility, and evolved gas gravity.
begin{aligned} MAE=, & frac{sum limits _{i=1}^N |y_i-widehat{y_i}|}{N} end{aligned} (1) begin{aligned} MAPE=, & frac{sum limits _{i=1}^N |y_i-widehat{y_i}|}{N} times 100% end{aligned} (2 Smaller values of the MAE and MAPE indicate better forecast accuracy.
3) On each label, five out of six syndromes of ML-kNN have better forecast accuracy; the accuracy of ML-kNN is some lower than kNN and RankSVM only in z1 and z4 syndrome.
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The proposed forecasting approach gives better forecasting accuracy: the MAPE has 8.1133% average value.
The proposed forecasting approach with input-data subset #5 gives better forecasting accuracy: the MAPE has 8.1133% average value.
In another aspect, Azizi [1] shows that ANFIS model provides better forecasting accuracy in comparison with Bayesian model.
They found that GARCH 1, 1) with GED had better forecasting accuracy compared to that using Student's t-distribution.
However, in absolute terms, the univariate models have better forecasting accuracy with respect to short term horizons.
This is not the case for the 1-year ahead forecasts (columns 5 and 6), where pension funds show a better forecasting accuracy.
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