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dÿÿ"dÿÿDdÿÿunable to allocate shape and strides.statsmodels/tsa/_innovations.pyxrtol must be a non-negative float or None.no default __reduce__ due to non-trivial __cinit__got differing extents in dimension %d (got %d and %d)Unable to convert item to objectOut of bounds on buffer access (axis %d)Invalid mode, expected 'c' or 'fortran', got %sIndirect dimensions not supportedIncompatible checksums (%s vs 0xb068931 = (name))Empty shape tuple for cython.arrayCannot create writable memory view from read-only memoryviewCannot assign to read-only memoryviewCan only create a buffer that is contiguous in memory.Buffer view does not expose stridestheta must be (nobs, q) where q is the moder ordernobs must be a positive integer
    innovations_algo(acov, nobs=None, rtol=None)

    Innovations algorithm to convert autocovariances to MA parameters.

    Parameters
    ----------
    acov : array_like
        Array containing autocovariances including lag 0.
    nobs : int, optional
        Number of periods to run the algorithm.  If not provided, nobs is
        equal to the length of acovf.
    rtol : float, optional
        Tolerance used to check for convergence. Default value is 0 which will
        never prematurely end the algorithm. Checks after 10 iterations and
        stops if sigma2[i] - sigma2[i - 10] < rtol * sigma2[0]. When the
        stopping condition is met, the remaining values in theta and sigma2
        are forward filled using the value of the final iteration.

    Returns
    -------
    theta : ndarray
        Innovation coefficients of MA representation. Array is (nobs, q) where
        q is the largest index of a non-zero autocovariance. theta
        corresponds to the first q columns of the coefficient matrix in the
        common description of the innovation algorithm.
    sigma2 : ndarray
        The prediction error variance (nobs,).

    See Also
    --------
    innovations_filter : Filter a series using the innovations algorithm.

    References
    ----------
    .. [*] Brockwell, P.J. and Davis, R.A., 2016. Introduction to time series
        and forecasting. Springer.

    Examples
    --------
    >>> import statsmodels.api as sm
    >>> data = sm.datasets.macrodata.load_pandas()
    >>> rgdpg = data.data['realgdp'].pct_change().dropna()
    >>> acov = sm.tsa.acovf(rgdpg)
    >>> nobs = activity.shape[0]
    >>> theta, sigma2  = innovations_algo(acov[:4], nobs=nobs)
    <strided and direct or indirect>
    innovations_filter(endog, theta)

    Filter observations using the innovations algorithm.

    Parameters
    ----------
    endog : array_like
        The time series to filter (nobs,). Should be demeaned if not mean 0.
    theta : ndarray
        Innovation coefficients of MA representation. Array must be (nobs, q)
        where q order of the MA.

    Returns
    -------
    ndarray
        Array of filtered innovations.

    See Also
    --------
    innovations_algo : Convert autocovariances to MA parameters.

    References
    ----------
    .. [*] Brockwell, P.J. and Davis, R.A., 2016. Introduction to time series
        and forecasting. Springer.

    Examples
    --------
    >>> import statsmodels.api as sm
    >>> data = sm.datasets.macrodata.load_pandas()
    >>> rgdpg = data.data['realgdp'].pct_change().dropna()
    >>> acov = sm.tsa.acovf(rgdpg)
    >>> nobs = activity.shape[0]
    >>> theta, sigma2  = innovations_algo(acov[:4], nobs=nobs)
    >>> resid = innovations_filter(rgdpg, theta)
    unable to allocate array data.statsmodels.tsa._innovationsstatsmodels.tools.validationitemsize <= 0 for cython.arrayinnovations_filter (line 100)Invalid shape in axis %d: %d.Cannot index with type '%s'innovations_algo (line 9)<contiguous and indirect><MemoryView of %r at 0x%x><MemoryView of %r object><contiguous and direct><strided and indirect><strided and direct>innovations_filtercline_in_traceback__pyx_unpickle_Enuminnovations_algo__setstate_cython____pyx_PickleErrordtype_is_objectallocate_bufferView.MemoryView__reduce_cython____pyx_getbufferPandasWrapperstringsource__pyx_checksumPickleErrorMemoryError__pyx_vtable____pyx_resultfloat_likecontiguousarray_likeValueErrorIndexError__reduce_ex____pyx_stateenumerateTypeError__setstate____pyx_typeoptionalitemsizeint_like__getstate__argwhereEllipsis_thetan_thetamemviewmax_lagfortran_endogasarrayupdateunpackstruct_rtol__reduce__pickle__name____import__formatencode_acovzerosthetastartshaperangenumpyflagserrorendogempty__class__ASCIIwrap__test__stopstepsizertolpacknobsndimnamemode__main____dict__baseacov_usubobj__new__maxhatpwnpidvunkjicOBuffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'memviewslice is already initialized!Unable to initialize pickling for %sDoes not understand character buffer dtype format string ('%c')statsmodels/tsa/_innovations.cView.MemoryView.memoryview.__str__View.MemoryView.memoryview.size.__get__View.MemoryView.memoryview.nbytes.__get__View.MemoryView.memoryview.itemsize.__get__View.MemoryView.memoryview.ndim.__get__View.MemoryView.memoryview.suboffsets.__get__View.MemoryView.memoryview.shape.__get__View.MemoryView.memoryview.setitem_indexedView.MemoryView.array.memview.__get__View.MemoryView.array.__getattr__View.MemoryView.array.__getitem__View.MemoryView.array.__setitem__Subscript deletion not supported by %.200sView.MemoryView.memoryview.assign_item_from_objectView.MemoryView._memoryviewslice.assign_item_from_object%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'%.200s() takes %.8s %zd positional argument%.1s (%zd given)Unexpected format string character: '%c'Expected a dimension of size %zu, got %zuExpected %d dimensions, got %dPython does not define a standard format string size for long double ('g')..Buffer dtype mismatch; next field is at offset %zd but %zd expectedBig-endian buffer not supported on little-endian compilerBuffer acquisition: Expected '{' after 'T'Cannot handle repeated arrays in format stringExpected a dimension of size %zu, got %dExpected a comma in format string, got '%c'Expected %d dimension(s), got %dUnexpected end of format string, expected ')'__int__ returned non-int (type %.200s).  The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__%.4s__ returned non-%.4s (type %.200s)calling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionView.MemoryView.array.__reduce_cython__View.MemoryView.array.__setstate_cython__View.MemoryView.memoryview.__reduce_cython__View.MemoryView.memoryview.__setstate_cython__View.MemoryView.memoryview.strides.__get__View.MemoryView._memoryviewslice.__reduce_cython__View.MemoryView._memoryviewslice.__setstate_cython__PyObject_GetBuffer: view==NULL argument is obsoleteView.MemoryView.array.__getbuffer__View.MemoryView.memoryview.__getbuffer__Acquisition count is %d (line %d)View.MemoryView.array.get_memviewView.MemoryView.memoryview_cwrapperBuffer has wrong number of dimensions (expected %d, got %d)Item size of buffer (%zu byte%s) does not match size of '%s' (%zu byte%s)Buffer is not indirectly contiguous in dimension %d.Buffer and memoryview are not contiguous in the same dimension.C-contiguous buffer is not contiguous in dimension %dC-contiguous buffer is not indirect in dimension %dBuffer exposes suboffsets but no stridesBuffer not compatible with direct access in dimension %d.Buffer is not indirectly accessible in dimension %d.View.MemoryView.memoryview.is_sliceView.MemoryView.memoryview.convert_item_to_objectView.MemoryView._memoryviewslice.convert_item_to_objectvalue too large to convert to intView.MemoryView.Enum.__reduce_cython__View.MemoryView.memoryview.__repr__View.MemoryView.memoryview_fromsliceView.MemoryView.memoryview_copy_from_slicec-string too long to convert to PythonStep may not be zero (axis %d)All dimensions preceding dimension %d must be indexed and not slicedView.MemoryView.slice_memviewsliceCannot convert %.200s to %.200sView.MemoryView.get_slice_from_memviewView.MemoryView.memoryview.is_f_contigView.MemoryView.memoryview.is_c_contigView.MemoryView.assert_direct_dimensionsView.MemoryView.memoryview.setitem_slice_assign_scalar'NoneType' object is not subscriptablehasattr(): attribute name must be stringView.MemoryView.__pyx_unpickle_Enum__set_stateView.MemoryView.Enum.__setstate_cython__Cannot transpose memoryview with indirect dimensionsView.MemoryView.transpose_memsliceView.MemoryView.copy_data_to_tempView.MemoryView.memoryview_copy_contentsCannot copy memoryview slice with indirect dimensions (axis %d)View.MemoryView.array_cwrapperView.MemoryView.memoryview.copy_fortranView.MemoryView.memoryview.copyView.MemoryView.memoryview_copyView.MemoryView.memoryview.T.__get__View.MemoryView.__pyx_unpickle_Enuminteger division or modulo by zerovalue too large to perform divisionView.MemoryView.pybuffer_indexView.MemoryView.memoryview.get_item_pointertoo many values to unpack (expected %zd)need more than %zd value%.1s to unpack'NoneType' object is not iterableView.MemoryView.memoryview.__setitem__View.MemoryView.array.__cinit__Argument '%.200s' must not be Noneobject of type 'NoneType' has no len()expected bytes, NoneType foundArgument '%.200s' has incorrect type (expected %.200s, got %.200s)View.MemoryView.memoryview.__cinit__View.MemoryView.memoryview.setitem_slice_assignmentstatsmodels/tsa/_innovations.pyxstatsmodels.tsa._innovations.innovations_filterlocal variable '%s' referenced before assignmentView.MemoryView.memoryview.__getitem__statsmodels.tsa._innovations.innovations_algocompiletime version %s of module '%.100s' does not match runtime version %sinit statsmodels.tsa._innovationsstatsmodels.tsa._innovations._memoryviewsliceInternal class for passing memoryview slices to Pythonstatsmodels.tsa._innovations.memoryviewstatsmodels.tsa._innovations.Enumstatsmodels.tsa._innovations.arrayð¿ð?
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    innovations_filter(endog, theta)

    Filter observations using the innovations algorithm.

    Parameters
    ----------
    endog : array_like
        The time series to filter (nobs,). Should be demeaned if not mean 0.
    theta : ndarray
        Innovation coefficients of MA representation. Array must be (nobs, q)
        where q order of the MA.

    Returns
    -------
    ndarray
        Array of filtered innovations.

    See Also
    --------
    innovations_algo : Convert autocovariances to MA parameters.

    References
    ----------
    .. [*] Brockwell, P.J. and Davis, R.A., 2016. Introduction to time series
        and forecasting. Springer.

    Examples
    --------
    >>> import statsmodels.api as sm
    >>> data = sm.datasets.macrodata.load_pandas()
    >>> rgdpg = data.data['realgdp'].pct_change().dropna()
    >>> acov = sm.tsa.acovf(rgdpg)
    >>> nobs = activity.shape[0]
    >>> theta, sigma2  = innovations_algo(acov[:4], nobs=nobs)
    >>> resid = innovations_filter(rgdpg, theta)
    
    innovations_algo(acov, nobs=None, rtol=None)

    Innovations algorithm to convert autocovariances to MA parameters.

    Parameters
    ----------
    acov : array_like
        Array containing autocovariances including lag 0.
    nobs : int, optional
        Number of periods to run the algorithm.  If not provided, nobs is
        equal to the length of acovf.
    rtol : float, optional
        Tolerance used to check for convergence. Default value is 0 which will
        never prematurely end the algorithm. Checks after 10 iterations and
        stops if sigma2[i] - sigma2[i - 10] < rtol * sigma2[0]. When the
        stopping condition is met, the remaining values in theta and sigma2
        are forward filled using the value of the final iteration.

    Returns
    -------
    theta : ndarray
        Innovation coefficients of MA representation. Array is (nobs, q) where
        q is the largest index of a non-zero autocovariance. theta
        corresponds to the first q columns of the coefficient matrix in the
        common description of the innovation algorithm.
    sigma2 : ndarray
        The prediction error variance (nobs,).

    See Also
    --------
    innovations_filter : Filter a series using the innovations algorithm.

    References
    ----------
    .. [*] Brockwell, P.J. and Davis, R.A., 2016. Introduction to time series
        and forecasting. Springer.

    Examples
    --------
    >>> import statsmodels.api as sm
    >>> data = sm.datasets.macrodata.load_pandas()
    >>> rgdpg = data.data['realgdp'].pct_change().dropna()
    >>> acov = sm.tsa.acovf(rgdpg)
    >>> nobs = activity.shape[0]
    >>> theta, sigma2  = innovations_algo(acov[:4], nobs=nobs)
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