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In this paper, we investigate the asymptotical behavior for a partial sum sequence of independent random variables, and we derive a law of the iterated logarithm type.
D-PNN could create better long-time models based on a partial sum DE solution, than a standard ANN time-series prediction (based on entire pattern definitions too).
We define the function S k which is a partial sum of f ∈ A by S k ( z ) = z + ( a k k ) z k, k ≥ 2, a k ≠ 0. (2).
In such a case, the solution can be found approximately as a partial sum of the series, (u_{n} overline{x},t)=sum_{k=0}^{n}f _{k} overline{x}) t^{alpha k}) in some reasonable interval of t, and thus the overall errors can be made smaller by adding more new terms as shown in the following case.
Finally, in practice one wants to approximate the function with a finite number of terms, let's say with a Taylor polynomial or a partial sum of the trigonometric series, respectively.
Each such value is exactly S z, j) plus a partial sum of the values in v g.
The C3 algorithm also uses a two-day guard band, but calculates a partial sum for the last three days of the positive deviation of the current value from the mean [ 6].
Similar(4)
In the on-line stage, the 2-hop similarities are computed, and a pruning algorithm is developed to support fast query processing through searching similar entries from a partial sums index derived from the 1-hop similarities.
D-PNN can approximate a multi-parametric function through a general partial sum DE solution (3).
To check the proportional hazards assumption, a score process (which is a transformed partial sum process of the martingale residuals) was compared with the simulated processes under the null hypothesis that the proportional hazards assumption holds (Lin et al, 1993).
Figure 2 Boundary behavior along ∂ − A ( a ) and ∂ + A ( b ) of the partial sum U N of order N = 22 representing the solution of the Dirichlet problem for the Laplace equation in the supershaped annulus A described by the Gielis formula with parameters k x ± = k y ± = 3, d x − = d y − = 3 / 4, d x + = d y + = 5 / 2, ν x ± = ν y ± = 12, ν 0 ± = 21.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com