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An RR tachograph is a graph of the numerical value of the RR-interval versus time. In the context of RR tachography , a Poincaré plot is a graph of RR( n ) on the x -axis versus RR( n + 1) (the succeeding RR interval) on the y -axis, i.e. one takes a sequence of intervals and plots each interval against the following interval. [ 3 ]
SSA's applicability to any kind of stationary or deterministically trending series has been extended to the case of a series with a stochastic trend, also known as a series with a unit root. In Hassani and Thomakos (2010) and Thomakos (2010) the basic theory on the properties and application of SSA in the case of series of a unit root is given ...
In time series analysis, Bartlett's method (also known as the method of averaged periodograms [1]), is used for estimating power spectra.It provides a way to reduce the variance of the periodogram in exchange for a reduction of resolution, compared to standard periodograms.
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. Generally, time series data is modelled as a stochastic process.
In graph theory and theoretical computer science, the longest path problem is the problem of finding a simple path of maximum length in a given graph.A path is called simple if it does not have any repeated vertices; the length of a path may either be measured by its number of edges, or (in weighted graphs) by the sum of the weights of its edges.
It measures the diversity of self-avoiding walks which start from a given node. A walk on a network is a sequence of adjacent vertices; a self-avoiding walk visits (lists) each vertex at most once. The original work used simulated walks of length 60 to characterize the network of urban streets in a Brazilian city. [6]
Nor will rank order statistics distinguish between these series. Yet series A is perfectly regular: knowing a term has the value of 1 enables one to predict with certainty that the next term will have the value of 0. In contrast, series B is randomly valued: knowing a term has the value of 1 gives no insight into what value the next term will have.
In time series analysis, the moving-average model (MA model), also known as moving-average process, is a common approach for modeling univariate time series. [ 1 ] [ 2 ] The moving-average model specifies that the output variable is cross-correlated with a non-identical to itself random-variable.