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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