Statistical parameters are not constant along a time series: mean or variance can vary each year, or during particular intervals (radical or smooth changes due to a pollution, a very cold winter, a shift in the system behaviour, etc. Sliding statistics offer the potential to describe series on successive blocs defined along the space-time axis
Calculate and plot an histogram of the distances between interpolated observations in a regulated time series and closest observations in the initial irregular time series. This allows to optimise the tol parameter
tsd
(Package: pastecs) :
Decomposition of one or several regular time series using various methods
Use a decomposition method to split the series into two or more components. Decomposition methods are either series filtering/smoothing (difference, average, median, evf), deseasoning (loess) or model-based decomposition (reg, i.e., regression).
last
(Package: pastecs) :
Get the last element of a vector
Extract the last element of a vector. Useful for the turnogram() function
● Data Source:
CranContrib
● Keywords: manip
● Alias: last
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vario
(Package: pastecs) :
Compute and plot a semi-variogram
Compute a classical semi-variogram for a single regular time series
● Data Source:
CranContrib
● Keywords: ts
● Alias: vario
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0 images
disto
(Package: pastecs) :
Compute and plot a distogram
A distogram is an extension of the variogram to a multivariate time-series. It computes, for each observation (with a constant interval h between each observation), the euclidean distance normated to one (chord distance)
turnogram
(Package: pastecs) :
Calculate and plot a turnogram for a regular time series
The turnogram is the variation of a monotony index with the observation scale (the number of data per time unit). A monotony index indicates if the series has more or less erratic variations than a pure random succession of independent observations. Since a time series almost always has autocorrelation, it is expected to be more monotonous than a purely random series. The monotony index is a way to quantify the density of information beared by a time series. The turnogram determines at which observation scale this density of information is maximum. It is also the scale that optimize the sampling effort (best compromise between less samples versus more information).
decaverage
(Package: pastecs) :
Time series decomposition using a moving average
Decompose a single regular time series with a moving average filtering. Return a 'tsd' object. To decompose several time series at once, use tsd() with the argument method="average"
providing 
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