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# scikits.statsmodels.sandbox.tsa.movstat

using scipy signal and numpy correlate to calculate some time series statistics

## original developer notes

see also scikits.timeseries (movstat is partially inspired by it) (added 2009-08-29: timeseries moving stats are in c, autocorrelation similar to here I thought I saw moving stats somewhere in python, maybe not)

TODO:

moving statistics * filters don’t handle boundary conditions nicely (correctly ?)

> e.g. minimum order filter uses 0 for out of bounds value -> append and prepend with last resp. first value

- enhance for nd arrays, with axis = 0

Note: Equivalence for 1D signals >>> np.all(signal.correlate(x,[1,1,1],’valid’)==np.correlate(x,[1,1,1])) True >>> np.all(ndimage.filters.correlate(x,[1,1,1], origin = -1)[:-3+1]==np.correlate(x,[1,1,1])) True

# multidimensional, but, it looks like it uses common filter across time series, no VAR ndimage.filters.correlate(np.vstack([x,x]),np.array([[1,1,1],[0,0,0]]), origin = 1) ndimage.filters.correlate(x,[1,1,1],origin = 1)) ndimage.filters.correlate(np.vstack([x,x]),np.array([[0.5,0.5,0.5],[0.5,0.5,0.5]]), origin = 1)

    >>> np.all(ndimage.filters.correlate(np.vstack([x,x]),np.array([[1,1,1],[0,0,0]]), origin = 1)[0]==ndimage.filters.correlate(x,[1,1,1],origin = 1))
    True
    >>> np.all(ndimage.filters.correlate(np.vstack([x,x]),np.array([[0.5,0.5,0.5],[0.5,0.5,0.5]]), origin = 1)[0]==ndimage.filters.correlate(x,[1,1,1],origin = 1))

update: 2009-09-06: cosmetic changes, rearrangements

Functions

`acf`(x[, unbiased]) | autocorrelation function for 1d
`acovf`(x[, unbiased, demean]) | autocovariance for 1D
`assert_array_almost_equal`(x, y[, decimal, ...]) | Raise an assertion if two objects are not equal up to desired precision.
`assert_array_equal`(x, y[, err_msg, verbose]) | Raise an assertion if two array_like objects are not equal.
`ccf`(x, y[, unbiased]) | cross-correlation function for 1d
`ccovf`(x, y[, unbiased, demean]) | crosscovariance for 1D
`check_movorder`() | graphical test for movorder
`expandarr`(x, k) |
`movmean`(x[, windowsize, lag]) | moving window mean
`movmoment`(x, k[, windowsize, lag]) | non-central moment
`movorder`(x[, order, windsize, lag]) | moving order statistics
`movvar`(x[, windowsize, lag]) | moving window variance
`pacf_ols`(x[, maxlag]) | Partial autocorrelation estimated with non-recursive OLS
`pacf_yw`(x[, maxlag, method]) | Partial autocorrelation estimated with non-recursive yule_walker

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