Noise

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Noise

Price and volume fluctuations that can confuse interpretation of market direction. Used in the context of general equities. Stock market activity caused by program trades, dividend rolls, and other phenomena not reflective of general sentiment. Antithesis of real.

Noise

A slight uptick or downtick in a security's or market's price and/or volume representing little or no actual change in its fundamentals. Noise occurs in the short-term; if noise continues in a certain direction, it becomes a trend, and, therefore, an indication of the general direction of the security or market. Noise, on the other hand, means little or nothing.

noise

Random market fluctuations that make it difficult to forecast the market's direction.
References in periodicals archive ?
For instance, electronic medical records (EMRs) illustrate well the need for data cleaning as it may provide noisy data containing incomplete information.
Here the regularization operator R[gamma] applied to noisy data [y.sup.[delta]] is given by
The MKL-BP model keeps the sparsity of [L.sub.1]-MKL model and GMKL model, which only selects useful kernels and makes relatively higher classification accuracy when faced with the noisy data. We use the Taylor expansion to optimize the problem.
RF effectively deals with noisy data through (1) bootstrap aggregation, (2) random selection of bands at each node, and (3) learning many variable unbiased decision trees [14].
The first step is to choose a specific instance selection method for removing some of the noisy data from the complete subset [D.sub.complete].
Example 1: (a) the input data g(t), (b) the direct computational result with noisy data
From Table 3, we find that the inversion is satisfactory in the case of using random noisy data, and the inversion errors become small when reducing the noise level.
where [D.sub.n] is the noisy data set composed by the 15 instances.
Exploiting multiple sparse domains: Reconstructions of real part of image from 5% noisy data with 15 views, when real and imaginary parts have similar physical boundaries, for different angular coverages: (a) 180[degrees], (b) 150[degrees], (c) 120[degrees], and (d) 90[degrees].
Reconstruction of the heat source with noisy data for Example 1.
As showed in Figure 1, to evaluate the robustness of mentioned algorithms to noisy data, all the images were occluded with local and global noise.
Information is comprehended and applied through fundamentally new methods of artificial intelligence that seek insights through algorithms using massive, noisy data sets.

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