Search results
Results From The WOW.Com Content Network
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 ]
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.
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.
the name being due to the tent-like shape of the graph of f μ. For the values of the parameter μ within 0 and 2, f μ maps the unit interval [0, 1] into itself, thus defining a discrete-time dynamical system on it (equivalently, a recurrence relation). In particular, iterating a point x 0 in [0, 1] gives rise to a sequence :
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]
In general, a distance matrix is a weighted adjacency matrix of some graph. In a network, a directed graph with weights assigned to the arcs, the distance between two nodes of the network can be defined as the minimum of the sums of the weights on the shortest paths joining the two nodes (where the number of steps in the path is bounded). [2]
Singular spectrum analysis applied to a time-series F, with reconstructed components grouped into trend, oscillations, and noise. In time series analysis, singular spectrum analysis (SSA) is a nonparametric spectral estimation method.
Horizontal axis: time of year; for example, with monthly data, all the January values are plotted (in chronological order), then all the February values, and so on. The horizontal line displays the mean value for each month over the time series. The analyst must specify the length of the seasonal pattern before generating this plot.