Noise

(redirected from noisy data)
Also found in: Dictionary, Thesaurus, Medical, Legal, Encyclopedia.

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 ?
In this section, we analyze the performance of our method by applying it to a bench mark of three illustrative examples of 2D and 3D noisy data points.
As indicated, the obtained results are still acceptable even with noisy data of [delta] = 0.
The noisy data in the critical zone airflow rate (consequently in R[a.
Upcoming observations of Abell 520 with Hubble should indicate whether dark matter theory really has to be reassessed or if researchers are merely arguing about noisy data, says Clowe.
Curvature-sensitive smoothing that increases polygon mesh quality by polishing noisy data in flatter areas while preserving highly curved areas and details.
Information processing in classical ~von Neumann~ architectures is less efficient compared to biological counterparts when dealing with ill-posed problems and noisy data.
It may improve the design of mathematical procedures called error-correcting codes used in computers to interpret noisy data.
A technique known as pruning allows inductive systems to handle noisy data and to calculate probabilities.
Examples and applications in signal and information extraction from noisy data
This collection of advanced concepts (visualization, constraint handling, coping with noisy data, gradient-enhanced modelling, multi-fidelity analysis and multiple objectives) represents an invaluable reference manual for engineers and researchers active in the area.
This means using theory to derive a family of gravitational wave "templates" to serve as guides for processing the noisy data obtained at the LIGO and VIRGO detectors - at first to find the signal and then to identify its type.