This package supports two kinds of applicative windows sliding over sequential data.
- the sequential data (index ordered) contains (N) values, here indexed 1:N.
- the operational window size (W) is given and W <= N.
The familiar kind rolls over data and with each advance applies a function to the subsequence spanned by the window. We call this approach a rolling function.
- a rolling max-of-n or mean-of-n taken over a data sequence.
- a windowed volatility calculation applied over a time series.
vanilla rolling functions take a sequence of N (length) values and a window span W (count of indicies). and return a sequence of N - W + 1 elements (calculated summary values)
We offer a second kind of applicative window; one that preserves the length of the given data sequence in the length of the value sequence that results. We call this approach a running function.
- for most of the data, the corresponding rolling function applies
- to obtain the remaining values, a tapering version of the function is applied.
vanilla running functions take a sequence of N (length) values and a window span W (count of indicies). and return a sequence of N elements (calculated summary values)
There is more :) see the README
- arbitrary and normalized weights may be used within a window
- there are an assortment of predefined rolling and running functions
- you may define your own rolling / running functions easily
- covariance and correlation is available for paired data sequences
- you may define your own rolling / running functions of two data streams