Search results
Results from the WOW.Com Content Network
This is an important technique for all types of time series analysis, especially for seasonal adjustment. [2] It seeks to construct, from an observed time series, a number of component series (that could be used to reconstruct the original by additions or multiplications) where each of these has a certain characteristic or type of behavior.
It can decompose the original monthly rainfall time series into various sub-series corresponding to different frequency. This decomposition is instrumental in unveiling hidden patterns and trends within the data, which can be crucial for improving the forecasting accuracy.
Decomposition of time series, a statistical task that deconstructs a time series into several components; Doob decomposition theorem of an integrable, discrete-time stochastic process; Doob–Meyer decomposition theorem of a continuous-time sub- or supermartingale
Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. Unsourced material may be challenged and removed. Find sources: "Decomposition" computer science – news · newspapers · books · scholar · JSTOR ( November 2008 ) ( Learn how and when to remove this message )
If two matrices of order n can be multiplied in time M(n), where M(n) ≥ n a for some a > 2, then an LU decomposition can be computed in time O(M(n)). [15] This means, for example, that an O(n 2.376) algorithm exists based on the Coppersmith–Winograd algorithm.
In linear algebra, the Cholesky decomposition or Cholesky factorization (pronounced / ʃ ə ˈ l ɛ s k i / shə-LES-kee) is a decomposition of a Hermitian, positive-definite matrix into the product of a lower triangular matrix and its conjugate transpose, which is useful for efficient numerical solutions, e.g., Monte Carlo simulations.
A well-known example implicitly using additive state decomposition is the superposition principle, widely used in physics and engineering. The superposition principle states: For all linear systems, the net response at a given place and time caused by two or more stimuli is the sum of the responses which would have been caused by each stimulus individually.
The first idea behind the Proper Orthogonal Decomposition (POD), as it was originally formulated in the domain of fluid dynamics to analyze turbulences, is to decompose a random vector field u(x, t) into a set of deterministic spatial functions Φ k (x) modulated by random time coefficients a k (t) so that: