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Therefore, DGSR algorithm that prunes data residues in the iterative process according to both sparsity and the group clustering trend is appropriate for our research.
DGSR algorithm is first explored in the proposed method to ensure the satisfactory performance when SR-based fusion approach is implemented on low-frequency components.
the FS statistic of DGSR in Table 3) often varies month to month and differs from one index to another.
Weighting factors for FS statistics [T.sub.max] [T.sub.min] [T.sub.ma] [RH.sub.min] [RH.sub.ma] 1/24 1/24 3/24 1/24 2/24 [T.sub.max] [W.sub.max] [W.sub.ma] DGSR 1/24 2/24 2/24 12/24 Table 3.
After converting the variables to stationary series, univariate models were formed in which three series, DGSR, DPVAR, and DANTI, were each regressed successively on a maximum of six lags of itself, and the optimal lag length was determined by selecting the lag that minimized the model's final prediction error, FPE [Akaike, 1969].
In one of these bivariate models, for example, DGSR was regressed on the optimum lags of itself determined in the previous step plus up to six lags of the inflation-rate variance.