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alkaline-ml / statsmodels python
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ñ¿ÿÓÿøÉøfÁøÃf ñ¸ Óçþ FÇÉñ¸ Óïþ FÇÉñ¸ Óçþ FÇÉøñÁÿ Óøþ GÇÉñ¸ HÓçHþ?HFÇà f. ñ¸ HÓïHþ?HFÇà f. ñ¸ HÓçHþ?HFÇà f. HøñHÁÿ?HÓøHþ?HGÇÐf. ñ¸ HÓçHþ?HFÇà f. ñ¸ HÓïHþ?HFÇà f. ñ¸ HÓçHþ?HFÇà f. HøñHÁÿ?HÓøHþ?HGÇà HìHÄà 'bool' 'char' 'signed char' 'unsigned char' 'short' 'unsigned short' 'int' 'unsigned int' 'long' 'unsigned long' 'long long' 'unsigned long long' 'float' 'double' 'long double' a struct Python object a pointer a string end unparseable format string 'complex long double' 'complex double' 'complex float' name '%U' is not defined Missing type object cannot import name %S %s (%s:%d) stringsource 'NoneType' is not iterable Expected %.16s, got %.200s exactly View.MemoryView.Enum.__init__ View.MemoryView._unellipsify buffer dtype an integer is required at least at most __cinit__ format shape View.MemoryView._err_dim Index out of bounds (axis %d) tuple memviewsliceobj View.MemoryView.memview_slice View.MemoryView._err View.MemoryView._err_extents Dimension %d is not direct __pyx_unpickle_Enum float division sarma_innovations_algo_fast carma_innovations_algo_fast darma_innovations_algo_fast zarma_innovations_algo_fast carma_transformed_acovf_fast zarma_transformed_acovf_fast carma_innovations_filter zarma_innovations zarma_innovations_filter darma_innovations_filter zarma_loglikeobs_fast darma_loglikeobs_fast darma_innovations darma_transformed_acovf_fast sarma_transformed_acovf_fast carma_loglikeobs_fast carma_innovations sarma_loglikeobs_fast sarma_innovations sarma_innovations_filter %d.%d %s builtins cython_runtime __builtins__ __init__.pxd type.pxd numpy flatiter broadcast ndarray ufunc numpy.core._multiarray_umath _ARRAY_API _ARRAY_API not found _ARRAY_API is NULL pointer numpy.import_array getbuffer(obj, view, flags) _arma_innovations base __reduce_cython__ __setstate_cython__ T strides suboffsets ndim itemsize nbytes is_c_contig is_f_contig copy copy_fortran memview __getattr__ double complex float complex float64_t float32_t êüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿêüÿ`ëüÿ`ëüÿPëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ@ëüÿ0ëüÿ`ëüÿ`ëüÿ ëüÿ`ëüÿ`ëüÿëüÿ ëüÿðêüÿ`ëüÿ`ëüÿàêüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿ`ëüÿÐêüÿÈêüÿ°êüÿ`ëüÿêüÿêüÿpêüÿ`êüÿ`ëüÿ`ëüÿPêüÿ`ëüÿ`ëüÿ`ëüÿ@êüÿ0êüÿ`ëüÿ@êüÿà=ýÿ>ýÿ>ýÿà=ýÿ>ýÿ>ýÿ>ýÿ>ýÿ>ýÿð=ýÿÐ=ýÿ>ýÿ>ýÿÀ=ýÿ>ýÿ>ýÿÀ=ýÿÀ=ýÿÀ=ýÿ>ýÿ>ýÿ>ýÿ>ýÿ>ýÿ>ýÿ>ýÿ>ýÿ>ýÿ>ýÿ>ýÿ>ýÿ>ýÿ>ýÿ>ýÿ>ýÿà=ýÿà=ýÿÀ=ýÿ>ýÿÐ=ýÿ >ýÿð=ýÿÐ=ýÿ>ýÿ>ýÿÀ=ýÿ>ýÿ>ýÿ>ýÿà=ýÿÀ=ýÿ>ýÿà=ýÿ²Aýÿ¼Aýÿ¼Aýÿ²Aýÿ¼Aýÿ¼Aýÿ¼Aýÿ¼Aýÿ¼Aýÿ²Aýÿ²Aýÿ¼Aýÿ¼Aýÿ²Aýÿ¼Aýÿ¼Aýÿ¤>ýÿ¤>ýÿ²Aýÿ¼Aýÿ¼Aýÿ¼Aýÿ¼Aýÿ¼Aýÿ¼Aýÿ¼Aýÿ¼Aýÿ¼Aýÿ¼Aýÿ¼Aýÿ¼Aýÿ¼Aýÿ¼Aýÿ¼Aýÿ¼AýÿAýÿAýÿAýÿ¼AýÿAýÿAýÿAýÿAýÿ¼Aýÿ¼AýÿAýÿ¼Aýÿ¼Aýÿ¼AýÿAýÿAýÿ¼AýÿAýÿx@ýÿ@ýÿ@ýÿx@ýÿ@ýÿ@ýÿ@ýÿ@ýÿ@ýÿ8?ýÿh@ýÿ@ýÿ@ýÿh@ýÿ@ýÿ@ýÿ(>ýÿ(>ýÿ(>ýÿ@ýÿ@ýÿ@ýÿ@ýÿ@ýÿ@ýÿ@ýÿ@ýÿ@ýÿ@ýÿ@ýÿ@ýÿ@ýÿ@ýÿ@ýÿ@ýÿx@ýÿx@ýÿ¸?ýÿ@ýÿ ?ýÿÐ?ýÿ8?ýÿh@ýÿ@ýÿ@ýÿh@ýÿ@ýÿ@ýÿ@ýÿx@ýÿ(>ýÿ@ýÿx@ýÿd@ýÿ´?ýÿ´?ýÿd@ýÿ´?ýÿ´?ýÿ´?ýÿ´?ýÿ´?ýÿT@ýÿ@ýÿ´?ýÿ´?ýÿD@ýÿ´?ýÿ´?ýÿD@ýÿD@ýÿD@ýÿ´?ýÿ´?ýÿ´?ýÿ´?ýÿ´?ýÿ´?ýÿ´?ýÿ´?ýÿ´?ýÿ´?ýÿ´?ýÿ´?ýÿ´?ýÿ´?ýÿ´?ýÿ´?ýÿd@ýÿd@ýÿ@ýÿ´?ýÿ´@ýÿt@ýÿT@ýÿ@ýÿ´?ýÿ´?ýÿD@ýÿ´?ýÿ´?ýÿ´?ýÿd@ýÿD@ýÿ´?ýÿd@ýÿBýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿÀAýÿØAýÿØAýÿÀAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿÀAýÿDýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØ@ýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿØAýÿ°DýÿØAýÿCýÿhDýÿDýÿPBýÿhDýÿØAýÿPBýÿØAýÿØAýÿØAýÿØAýÿØAýÿPBýÿPBýÿØAýÿØAýÿPBýÿØAýÿØAýÿPBýÿØAýÿPBýÿØAýÿØAýÿÈCýÿØAýÿØAýÿØAýÿØAýÿØAýÿCýÿØAýÿØAýÿØAýÿhDýÿØAýÿØAýÿØAýÿPBýÿPBýÿPBýÿØAýÿPBýÿPBýÿPBýÿPBýÿØAýÿØAýÿPBýÿØAýÿØAýÿØAýÿPBýÿPBýÿØAýÿ BýÿØAýÿØAýÿØAýÿØAýÿØBýÿØAýÿØAýÿØAýÿØAýÿ(Cýÿkýÿzkýÿqkýÿhkýÿ_kýÿVkýÿÒmýÿ0mýÿQmýÿrmýÿmýÿ þÿ þÿÿþÿöþÿíþÿäþÿþÿ$ þÿE þÿf þÿ þÿÅ$þÿ¼$þÿ³$þÿª$þÿ¡$þÿ$þÿ¸"þÿØ$þÿù$þÿ%þÿ;%þÿy-þÿp-þÿg-þÿ^-þÿU-þÿL-þÿl+þÿ-þÿ-þÿÎ-þÿï-þÿ-Bþÿ$BþÿBþÿBþÿ Bþÿ Bþÿ @þÿ@BþÿaBþÿBþÿ£Bþÿxþÿoþÿfþÿ]þÿTþÿ¦þÿ¦þÿ¦þÿy¦þÿp¦þÿ@¯þÿ7¯þÿ.¯þÿ%¯þÿ ¯þÿ ¸þÿ¸þÿú·þÿñ·þÿè·þÿÄÈþÿ¸Èþÿ¬Èþÿ ÈþÿÈþÿ@ ÿÿ4 ÿÿ( ÿÿ ÿÿ ÿÿ ÿÿ ÿÿ ÿÿ ÿÿü ÿÿ(jÿÿ jÿÿjÿÿjÿÿøiÿÿ8}ÿÿ/}ÿÿ&}ÿÿ }ÿÿ}ÿÿШÿÿĨÿÿ¸¨ÿÿ¬¨ÿÿ ¨ÿÿP½ÿÿG½ÿÿ>½ÿÿ5½ÿÿ,½ÿÿ ÆÿÿÆÿÿúÅÿÿñÅÿÿèÅÿÿ Format string allocated too short. unable to allocate shape and strides. numpy.core.umath failed to import no default __reduce__ due to non-trivial __cinit__ ndarray is not Fortran contiguous got differing extents in dimension %d (got %d and %d) Unable to convert item to object Out of bounds on buffer access (axis %d) Non-native byte order not supported Invalid mode, expected 'c' or 'fortran', got %s Indirect dimensions not supported Incompatible checksums (%s vs 0xb068931 = (name)) Format string allocated too short, see comment in numpy.pxd Empty shape tuple for cython.array Cannot create writable memory view from read-only memoryview Cannot assign to read-only memoryview Can only create a buffer that is contiguous in memory. Buffer view does not expose strides unknown dtype code in numpy.pxd (%d) numpy.core.multiarray failed to import
Innovations algorithm
Author: Chad Fulton
License: Simplified-BSD
<strided and direct or indirect> unable to allocate array data. ndarray is not C contiguous itemsize <= 0 for cython.array Invalid shape in axis %d: %d. Cannot index with type '%s' <contiguous and indirect> <MemoryView of %r at 0x%x> <MemoryView of %r object> <contiguous and direct> <strided and indirect> <strided and direct> cline_in_traceback __pyx_unpickle_Enum statsmodels.tsa __setstate_cython__ __pyx_PickleError dtype_is_object allocate_buffer View.MemoryView __reduce_cython__ __pyx_getbuffer arima_process stringsource __pyx_checksum RuntimeError PickleError MemoryError ImportError __pyx_vtable__ __pyx_result arma_acovf ValueError IndexError __reduce_ex__ __pyx_state ma_params enumerate complex64 ar_params TypeError warnings __setstate__ __pyx_type itemsize __getstate__ Ellipsis memview fortran float32 update unpack struct sigma2 __reduce__ pickle __name__ __import__ format encode acovf2 theta start shape range numpy flags error endog dtype __class__ array acovf ASCII __test__ stop step size pack nobs ndim name mode __main__ __dict__ base obj __new__ np ma id ar c O Buffer dtype mismatch, expected %s%s%s but got %s Buffer dtype mismatch, expected '%s' but got %s in '%s.%s' memviewslice is already initialized! Unable to initialize pickling for %s %.200s.%.200s is not a type object %.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject %s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject Does not understand character buffer dtype format string ('%c') Cannot convert %.200s to %.200s statsmodels/tsa/innovations/_arma_innovations.c View.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_indexed View.MemoryView.array.memview.__get__ View.MemoryView.array.__getattr__ View.MemoryView.array.__getitem__ View.MemoryView.array.__setitem__ Subscript deletion not supported by %.200s View.MemoryView.memoryview.assign_item_from_object View.MemoryView._memoryviewslice.assign_item_from_object __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) %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) calling %R should have returned an instance of BaseException, not %R raise: exception class must be a subclass of BaseException View.MemoryView.array.__reduce_cython__ View.MemoryView.array.__setstate_cython__ View.MemoryView.memoryview.convert_item_to_object View.MemoryView._memoryviewslice.convert_item_to_object 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 obsolete View.MemoryView.array.__getbuffer__ View.MemoryView.memoryview.__getbuffer__ Unexpected format string character: '%c' Expected a dimension of size %zu, got %zu Expected %d dimensions, got %d Python does not define a standard format string size for long double ('g').. Buffer dtype mismatch; next field is at offset %zd but %zd expected Big-endian buffer not supported on little-endian compiler Buffer acquisition: Expected '{' after 'T' Cannot handle repeated arrays in format string Expected a dimension of size %zu, got %d Expected a comma in format string, got '%c' Expected %d dimension(s), got %d Unexpected end of format string, expected ')' Acquisition count is %d (line %d) View.MemoryView.array.get_memview View.MemoryView.memoryview_cwrapper Buffer 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 %d C-contiguous buffer is not indirect in dimension %d Buffer exposes suboffsets but no strides Buffer not compatible with direct access in dimension %d. Buffer is not indirectly accessible in dimension %d. View.MemoryView.memoryview.is_slice too many values to unpack (expected %zd) View.MemoryView.Enum.__reduce_cython__ View.MemoryView.memoryview.__repr__ value too large to convert to int View.MemoryView.array.__cinit__ Argument '%.200s' must not be None object of type 'NoneType' has no len() expected bytes, NoneType found Argument '%.200s' has incorrect type (expected %.200s, got %.200s) c-string too long to convert to Python Step may not be zero (axis %d) All dimensions preceding dimension %d must be indexed and not sliced View.MemoryView.slice_memviewslice View.MemoryView.get_slice_from_memview View.MemoryView.memoryview.is_f_contig View.MemoryView.memoryview.is_c_contig View.MemoryView.assert_direct_dimensions View.MemoryView.memoryview.setitem_slice_assign_scalar 'NoneType' object is not subscriptable hasattr(): attribute name must be string View.MemoryView.__pyx_unpickle_Enum__set_state View.MemoryView.Enum.__setstate_cython__ View.MemoryView.memoryview_fromslice View.MemoryView.memoryview_copy_from_slice local variable '%s' referenced before assignment need more than %zd value%.1s to unpack 'NoneType' object is not iterable View.MemoryView.memoryview.__getitem__ statsmodels/tsa/innovations/_arma_innovations.pyx statsmodels.tsa.innovations._arma_innovations.darma_innovations_filter View.MemoryView.memoryview.__cinit__ Cannot transpose memoryview with indirect dimensions View.MemoryView.transpose_memslice View.MemoryView.copy_data_to_temp View.MemoryView.memoryview_copy_contents Cannot copy memoryview slice with indirect dimensions (axis %d) View.MemoryView.array_cwrapper View.MemoryView.memoryview.copy_fortran View.MemoryView.memoryview.copy View.MemoryView.memoryview_copy View.MemoryView.memoryview.T.__get__ statsmodels.tsa.innovations._arma_innovations.carma_innovations_filter statsmodels.tsa.innovations._arma_innovations.zarma_innovations_filter View.MemoryView.__pyx_unpickle_Enum statsmodels.tsa.innovations._arma_innovations.sarma_innovations_algo_fast statsmodels.tsa.innovations._arma_innovations.darma_innovations_algo_fast View.MemoryView.memoryview.__setitem__ integer division or modulo by zero value too large to perform division View.MemoryView.pybuffer_index View.MemoryView.memoryview.get_item_pointer statsmodels.tsa.innovations._arma_innovations.carma_innovations_algo_fast statsmodels.tsa.innovations._arma_innovations.zarma_innovations_algo_fast View.MemoryView.memoryview.setitem_slice_assignment statsmodels.tsa.innovations._arma_innovations.carma_transformed_acovf_fast statsmodels.tsa.innovations._arma_innovations.zarma_transformed_acovf_fast statsmodels.tsa.innovations._arma_innovations.zarma_innovations statsmodels.tsa.innovations._arma_innovations.zarma_loglikeobs_fast statsmodels.tsa.innovations._arma_innovations.darma_transformed_acovf_fast statsmodels.tsa.innovations._arma_innovations.darma_innovations statsmodels.tsa.innovations._arma_innovations.darma_loglikeobs_fast statsmodels.tsa.innovations._arma_innovations.sarma_transformed_acovf_fast statsmodels.tsa.innovations._arma_innovations.carma_innovations statsmodels.tsa.innovations._arma_innovations.carma_loglikeobs_fast statsmodels.tsa.innovations._arma_innovations.sarma_innovations_filter statsmodels.tsa.innovations._arma_innovations.sarma_innovations statsmodels.tsa.innovations._arma_innovations.sarma_loglikeobs_fast statsmodels.tsa.innovations._arma_innovations compiletime version %s of module '%.100s' does not match runtime version %s _ARRAY_API is not PyCapsule object module compiled against ABI version 0x%x but this version of numpy is 0x%x module compiled against API version 0x%x but this version of numpy is 0x%x FATAL: module compiled as unknown endian FATAL: module compiled as little endian, but detected different endianness at runtime init statsmodels.tsa.innovations._arma_innovations statsmodels.tsa.innovations._arma_innovations._memoryviewslice Internal class for passing memoryview slices to Python statsmodels.tsa.innovations._arma_innovations.memoryview statsmodels.tsa.innovations._arma_innovations.Enum statsmodels.tsa.innovations._arma_innovations.array ð¿ ð?-DTû!@ à¿ à?´¾dÈñgý? 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zarma_loglikeobs_fast(cnp.complex128_t [:] endog, cnp.complex128_t [:] ar_params, cnp.complex128_t [:] ma_params, cnp.complex128_t sigma2)
Quickly calculate the loglikelihood of each observation for an ARMA process
Parameters
----------
endog : ndarray
The observed time-series process.
ar_params : ndarray
Autoregressive parameters.
ma_params : ndarray
Moving average parameters.
sigma2 : ndarray
The ARMA innovation variance.
Returns
-------
loglike : ndarray of float
Array of loglikelihood values for each observation.
Notes
-----
Details related to computing the loglikelihood associated with an ARMA
process using the innovations algorithm are given in _[1] section 5.2.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
arma_innovations(cnp.complex128_t [:] endog, cnp.complex128_t [:] ar_params, cnp.complex128_t [:] ma_params):
Compute innovations and variances based on an ARMA process.
Parameters
----------
endog : ndarray
The observed time-series process.
ar_params : ndarray
Autoregressive parameters.
ma_params : ndarray
Moving average parameters.
sigma2 : ndarray
The ARMA innovation variance.
Returns
-------
u : ndarray
The vector of innovations: the one-step-ahead prediction errors from
applying the innovations algorithm.
v : ndarray
The vector of innovation variances.
Notes
-----
The innovations algorithm is presented in _[1], section 2.5.2.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
arma_innovations_filter(cnp.complex128_t [:] endog, cnp.complex128_t [:] ar_params, cnp.complex128_t [:] ma_params, cnp.complex128_t [:, :] theta):
Innovations filter for an ARMA process.
Parameters
----------
endog : ndarray
The observed time-series process.
ar_params : ndarray
Autoregressive parameters.
ma_params : ndarray
Moving average parameters.
theta : ndarray
The matrix of moving average coefficients from the innovations
algorithm (see `arma_innovations_algo` or `arma_innovations_algo_fast`)
Returns
-------
u : ndarray
The vector of innovations: the one-step-ahead prediction errors from
applying the innovations algorithm.
Notes
-----
The innovations algorithm is presented in _[1], section 2.5.2.
Details of the innovations algorithm applied to ARMA processes is given
in _[1] section 3.3 and in section 5.2.7.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
arma_innovations_algo_fast(int nobs, cnp.complex128_t [:] ar_params, cnp.complex128_t [:] ma_params, cnp.complex128_t [:, :] acovf, cnp.complex128_t [:] acovf2)
Quickly apply innovations algorithm for an ARMA process.
Parameters
----------
ar_params : ndarray
Autoregressive parameters.
ma_params : ndarray
Moving average parameters.
acovf : ndarray
An `m * 2` x `m * 2` autocovariance matrix at least the first `m`
columns filled in, where `m = max(len(ar_params), ma_params)`
(see `arma_transformed_acovf_fast`).
acovf2 : ndarray
A `max(0, nobs - m)` length vector containing the autocovariance
function associated with the final `nobs - m` observations.
Returns
-------
theta : ndarray
The matrix of moving average coefficients from the innovations
algorithm.
v : ndarray
The vector of mean squared errors.
Notes
-----
The innovations algorithm is presented in _[1], section 2.5.2.
Details of the innovations algorithm applied to ARMA processes is given
in _[1] section 3.3 and in section 5.2.7.
This function is relatively fast even with a large number of observations
since we can exploit a number of known zeros in the theta array.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
arma_transformed_acovf_fast(cnp.complex128_t [:] ar, cnp.complex128_t [:] ma, cnp.complex128_t [:] arma_acovf)
Quickly construct the autocovariance matrix for a transformed process.
Using the autocovariance function for an ARMA process, constructs the
autocovariances associated with the transformed process described
in equation (3.3.1) of _[1] in a memory efficient, and so fast, way.
Parameters
----------
ar : ndarray
Autoregressive coefficients, including the zero lag, where the sign
convention assumes the coefficients are part of the lag polynomial on
the left-hand-side of the ARMA definition (i.e. they have the opposite
sign from the usual econometrics convention in which the coefficients
are on the right-hand-side of the ARMA definition).
ma : ndarray
Moving average coefficients, including the zero lag, where the sign
convention assumes the coefficients are part of the lag polynomial on
the right-hand-side of the ARMA definition (i.e. they have the same
sign from the usual econometrics convention in which the coefficients
are on the right-hand-side of the ARMA definition).
arma_acovf : ndarray
The vector of autocovariances of the ARMA process.
Returns
-------
acovf : ndarray
A matrix containing the autocovariances of the portion of the
transformed process with time-varying autocovariances. Its dimension
is equal to `min(m * 2, n)` where `m = max(len(ar) - 1, len(ma) - 1)`
and `n` is the length of the input array `arma_acovf`. It is important
to note that only the values in the first `m` columns or `m` rows are
valid. In particular, the entries in the block `acovf[m:, m:]` should
not be used in any case (and in fact will always be equal to zeros).
acovf2 : ndarray
An array containing the autocovariance function of the portion of the
transformed process with time-invariant autocovariances. Its dimension
is equal to `max(n - m, 0)` where `n` is the length of the input
array `arma_acovf`.
Notes
-----
The definition of this autocovariance matrix is from _[1] equation 3.3.3.
This function assumes that the variance of the ARMA innovation term is
equal to one. If this is not true, then the calling program should replace
`arma_acovf` with `arma_acovf / sigma2`, where sigma2 is that variance.
This function is relatively fast even when `arma_acovf` is large, since
it only constructs the full autocovariance matrix for a generally small
subset of observations. The trade-off is that the output of this function
is somewhat more difficult to use.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
carma_loglikeobs_fast(cnp.complex64_t [:] endog, cnp.complex64_t [:] ar_params, cnp.complex64_t [:] ma_params, cnp.complex64_t sigma2)
Quickly calculate the loglikelihood of each observation for an ARMA process
Parameters
----------
endog : ndarray
The observed time-series process.
ar_params : ndarray
Autoregressive parameters.
ma_params : ndarray
Moving average parameters.
sigma2 : ndarray
The ARMA innovation variance.
Returns
-------
loglike : ndarray of float
Array of loglikelihood values for each observation.
Notes
-----
Details related to computing the loglikelihood associated with an ARMA
process using the innovations algorithm are given in _[1] section 5.2.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
arma_innovations(cnp.complex64_t [:] endog, cnp.complex64_t [:] ar_params, cnp.complex64_t [:] ma_params):
Compute innovations and variances based on an ARMA process.
Parameters
----------
endog : ndarray
The observed time-series process.
ar_params : ndarray
Autoregressive parameters.
ma_params : ndarray
Moving average parameters.
sigma2 : ndarray
The ARMA innovation variance.
Returns
-------
u : ndarray
The vector of innovations: the one-step-ahead prediction errors from
applying the innovations algorithm.
v : ndarray
The vector of innovation variances.
Notes
-----
The innovations algorithm is presented in _[1], section 2.5.2.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
arma_innovations_filter(cnp.complex64_t [:] endog, cnp.complex64_t [:] ar_params, cnp.complex64_t [:] ma_params, cnp.complex64_t [:, :] theta):
Innovations filter for an ARMA process.
Parameters
----------
endog : ndarray
The observed time-series process.
ar_params : ndarray
Autoregressive parameters.
ma_params : ndarray
Moving average parameters.
theta : ndarray
The matrix of moving average coefficients from the innovations
algorithm (see `arma_innovations_algo` or `arma_innovations_algo_fast`)
Returns
-------
u : ndarray
The vector of innovations: the one-step-ahead prediction errors from
applying the innovations algorithm.
Notes
-----
The innovations algorithm is presented in _[1], section 2.5.2.
Details of the innovations algorithm applied to ARMA processes is given
in _[1] section 3.3 and in section 5.2.7.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
arma_innovations_algo_fast(int nobs, cnp.complex64_t [:] ar_params, cnp.complex64_t [:] ma_params, cnp.complex64_t [:, :] acovf, cnp.complex64_t [:] acovf2)
Quickly apply innovations algorithm for an ARMA process.
Parameters
----------
ar_params : ndarray
Autoregressive parameters.
ma_params : ndarray
Moving average parameters.
acovf : ndarray
An `m * 2` x `m * 2` autocovariance matrix at least the first `m`
columns filled in, where `m = max(len(ar_params), ma_params)`
(see `arma_transformed_acovf_fast`).
acovf2 : ndarray
A `max(0, nobs - m)` length vector containing the autocovariance
function associated with the final `nobs - m` observations.
Returns
-------
theta : ndarray
The matrix of moving average coefficients from the innovations
algorithm.
v : ndarray
The vector of mean squared errors.
Notes
-----
The innovations algorithm is presented in _[1], section 2.5.2.
Details of the innovations algorithm applied to ARMA processes is given
in _[1] section 3.3 and in section 5.2.7.
This function is relatively fast even with a large number of observations
since we can exploit a number of known zeros in the theta array.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
arma_transformed_acovf_fast(cnp.complex64_t [:] ar, cnp.complex64_t [:] ma, cnp.complex64_t [:] arma_acovf)
Quickly construct the autocovariance matrix for a transformed process.
Using the autocovariance function for an ARMA process, constructs the
autocovariances associated with the transformed process described
in equation (3.3.1) of _[1] in a memory efficient, and so fast, way.
Parameters
----------
ar : ndarray
Autoregressive coefficients, including the zero lag, where the sign
convention assumes the coefficients are part of the lag polynomial on
the left-hand-side of the ARMA definition (i.e. they have the opposite
sign from the usual econometrics convention in which the coefficients
are on the right-hand-side of the ARMA definition).
ma : ndarray
Moving average coefficients, including the zero lag, where the sign
convention assumes the coefficients are part of the lag polynomial on
the right-hand-side of the ARMA definition (i.e. they have the same
sign from the usual econometrics convention in which the coefficients
are on the right-hand-side of the ARMA definition).
arma_acovf : ndarray
The vector of autocovariances of the ARMA process.
Returns
-------
acovf : ndarray
A matrix containing the autocovariances of the portion of the
transformed process with time-varying autocovariances. Its dimension
is equal to `min(m * 2, n)` where `m = max(len(ar) - 1, len(ma) - 1)`
and `n` is the length of the input array `arma_acovf`. It is important
to note that only the values in the first `m` columns or `m` rows are
valid. In particular, the entries in the block `acovf[m:, m:]` should
not be used in any case (and in fact will always be equal to zeros).
acovf2 : ndarray
An array containing the autocovariance function of the portion of the
transformed process with time-invariant autocovariances. Its dimension
is equal to `max(n - m, 0)` where `n` is the length of the input
array `arma_acovf`.
Notes
-----
The definition of this autocovariance matrix is from _[1] equation 3.3.3.
This function assumes that the variance of the ARMA innovation term is
equal to one. If this is not true, then the calling program should replace
`arma_acovf` with `arma_acovf / sigma2`, where sigma2 is that variance.
This function is relatively fast even when `arma_acovf` is large, since
it only constructs the full autocovariance matrix for a generally small
subset of observations. The trade-off is that the output of this function
is somewhat more difficult to use.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
darma_loglikeobs_fast(cnp.float64_t [:] endog, cnp.float64_t [:] ar_params, cnp.float64_t [:] ma_params, cnp.float64_t sigma2)
Quickly calculate the loglikelihood of each observation for an ARMA process
Parameters
----------
endog : ndarray
The observed time-series process.
ar_params : ndarray
Autoregressive parameters.
ma_params : ndarray
Moving average parameters.
sigma2 : ndarray
The ARMA innovation variance.
Returns
-------
loglike : ndarray of float
Array of loglikelihood values for each observation.
Notes
-----
Details related to computing the loglikelihood associated with an ARMA
process using the innovations algorithm are given in _[1] section 5.2.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
arma_innovations(cnp.float64_t [:] endog, cnp.float64_t [:] ar_params, cnp.float64_t [:] ma_params):
Compute innovations and variances based on an ARMA process.
Parameters
----------
endog : ndarray
The observed time-series process.
ar_params : ndarray
Autoregressive parameters.
ma_params : ndarray
Moving average parameters.
sigma2 : ndarray
The ARMA innovation variance.
Returns
-------
u : ndarray
The vector of innovations: the one-step-ahead prediction errors from
applying the innovations algorithm.
v : ndarray
The vector of innovation variances.
Notes
-----
The innovations algorithm is presented in _[1], section 2.5.2.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
arma_innovations_filter(cnp.float64_t [:] endog, cnp.float64_t [:] ar_params, cnp.float64_t [:] ma_params, cnp.float64_t [:, :] theta):
Innovations filter for an ARMA process.
Parameters
----------
endog : ndarray
The observed time-series process.
ar_params : ndarray
Autoregressive parameters.
ma_params : ndarray
Moving average parameters.
theta : ndarray
The matrix of moving average coefficients from the innovations
algorithm (see `arma_innovations_algo` or `arma_innovations_algo_fast`)
Returns
-------
u : ndarray
The vector of innovations: the one-step-ahead prediction errors from
applying the innovations algorithm.
Notes
-----
The innovations algorithm is presented in _[1], section 2.5.2.
Details of the innovations algorithm applied to ARMA processes is given
in _[1] section 3.3 and in section 5.2.7.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
arma_innovations_algo_fast(int nobs, cnp.float64_t [:] ar_params, cnp.float64_t [:] ma_params, cnp.float64_t [:, :] acovf, cnp.float64_t [:] acovf2)
Quickly apply innovations algorithm for an ARMA process.
Parameters
----------
ar_params : ndarray
Autoregressive parameters.
ma_params : ndarray
Moving average parameters.
acovf : ndarray
An `m * 2` x `m * 2` autocovariance matrix at least the first `m`
columns filled in, where `m = max(len(ar_params), ma_params)`
(see `arma_transformed_acovf_fast`).
acovf2 : ndarray
A `max(0, nobs - m)` length vector containing the autocovariance
function associated with the final `nobs - m` observations.
Returns
-------
theta : ndarray
The matrix of moving average coefficients from the innovations
algorithm.
v : ndarray
The vector of mean squared errors.
Notes
-----
The innovations algorithm is presented in _[1], section 2.5.2.
Details of the innovations algorithm applied to ARMA processes is given
in _[1] section 3.3 and in section 5.2.7.
This function is relatively fast even with a large number of observations
since we can exploit a number of known zeros in the theta array.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
arma_transformed_acovf_fast(cnp.float64_t [:] ar, cnp.float64_t [:] ma, cnp.float64_t [:] arma_acovf)
Quickly construct the autocovariance matrix for a transformed process.
Using the autocovariance function for an ARMA process, constructs the
autocovariances associated with the transformed process described
in equation (3.3.1) of _[1] in a memory efficient, and so fast, way.
Parameters
----------
ar : ndarray
Autoregressive coefficients, including the zero lag, where the sign
convention assumes the coefficients are part of the lag polynomial on
the left-hand-side of the ARMA definition (i.e. they have the opposite
sign from the usual econometrics convention in which the coefficients
are on the right-hand-side of the ARMA definition).
ma : ndarray
Moving average coefficients, including the zero lag, where the sign
convention assumes the coefficients are part of the lag polynomial on
the right-hand-side of the ARMA definition (i.e. they have the same
sign from the usual econometrics convention in which the coefficients
are on the right-hand-side of the ARMA definition).
arma_acovf : ndarray
The vector of autocovariances of the ARMA process.
Returns
-------
acovf : ndarray
A matrix containing the autocovariances of the portion of the
transformed process with time-varying autocovariances. Its dimension
is equal to `min(m * 2, n)` where `m = max(len(ar) - 1, len(ma) - 1)`
and `n` is the length of the input array `arma_acovf`. It is important
to note that only the values in the first `m` columns or `m` rows are
valid. In particular, the entries in the block `acovf[m:, m:]` should
not be used in any case (and in fact will always be equal to zeros).
acovf2 : ndarray
An array containing the autocovariance function of the portion of the
transformed process with time-invariant autocovariances. Its dimension
is equal to `max(n - m, 0)` where `n` is the length of the input
array `arma_acovf`.
Notes
-----
The definition of this autocovariance matrix is from _[1] equation 3.3.3.
This function assumes that the variance of the ARMA innovation term is
equal to one. If this is not true, then the calling program should replace
`arma_acovf` with `arma_acovf / sigma2`, where sigma2 is that variance.
This function is relatively fast even when `arma_acovf` is large, since
it only constructs the full autocovariance matrix for a generally small
subset of observations. The trade-off is that the output of this function
is somewhat more difficult to use.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
sarma_loglikeobs_fast(cnp.float32_t [:] endog, cnp.float32_t [:] ar_params, cnp.float32_t [:] ma_params, cnp.float32_t sigma2)
Quickly calculate the loglikelihood of each observation for an ARMA process
Parameters
----------
endog : ndarray
The observed time-series process.
ar_params : ndarray
Autoregressive parameters.
ma_params : ndarray
Moving average parameters.
sigma2 : ndarray
The ARMA innovation variance.
Returns
-------
loglike : ndarray of float
Array of loglikelihood values for each observation.
Notes
-----
Details related to computing the loglikelihood associated with an ARMA
process using the innovations algorithm are given in _[1] section 5.2.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
arma_innovations(cnp.float32_t [:] endog, cnp.float32_t [:] ar_params, cnp.float32_t [:] ma_params):
Compute innovations and variances based on an ARMA process.
Parameters
----------
endog : ndarray
The observed time-series process.
ar_params : ndarray
Autoregressive parameters.
ma_params : ndarray
Moving average parameters.
sigma2 : ndarray
The ARMA innovation variance.
Returns
-------
u : ndarray
The vector of innovations: the one-step-ahead prediction errors from
applying the innovations algorithm.
v : ndarray
The vector of innovation variances.
Notes
-----
The innovations algorithm is presented in _[1], section 2.5.2.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
arma_innovations_filter(cnp.float32_t [:] endog, cnp.float32_t [:] ar_params, cnp.float32_t [:] ma_params, cnp.float32_t [:, :] theta):
Innovations filter for an ARMA process.
Parameters
----------
endog : ndarray
The observed time-series process.
ar_params : ndarray
Autoregressive parameters.
ma_params : ndarray
Moving average parameters.
theta : ndarray
The matrix of moving average coefficients from the innovations
algorithm (see `arma_innovations_algo` or `arma_innovations_algo_fast`)
Returns
-------
u : ndarray
The vector of innovations: the one-step-ahead prediction errors from
applying the innovations algorithm.
Notes
-----
The innovations algorithm is presented in _[1], section 2.5.2.
Details of the innovations algorithm applied to ARMA processes is given
in _[1] section 3.3 and in section 5.2.7.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
arma_innovations_algo_fast(int nobs, cnp.float32_t [:] ar_params, cnp.float32_t [:] ma_params, cnp.float32_t [:, :] acovf, cnp.float32_t [:] acovf2)
Quickly apply innovations algorithm for an ARMA process.
Parameters
----------
ar_params : ndarray
Autoregressive parameters.
ma_params : ndarray
Moving average parameters.
acovf : ndarray
An `m * 2` x `m * 2` autocovariance matrix at least the first `m`
columns filled in, where `m = max(len(ar_params), ma_params)`
(see `arma_transformed_acovf_fast`).
acovf2 : ndarray
A `max(0, nobs - m)` length vector containing the autocovariance
function associated with the final `nobs - m` observations.
Returns
-------
theta : ndarray
The matrix of moving average coefficients from the innovations
algorithm.
v : ndarray
The vector of mean squared errors.
Notes
-----
The innovations algorithm is presented in _[1], section 2.5.2.
Details of the innovations algorithm applied to ARMA processes is given
in _[1] section 3.3 and in section 5.2.7.
This function is relatively fast even with a large number of observations
since we can exploit a number of known zeros in the theta array.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
arma_transformed_acovf_fast(cnp.float32_t [:] ar, cnp.float32_t [:] ma, cnp.float32_t [:] arma_acovf)
Quickly construct the autocovariance matrix for a transformed process.
Using the autocovariance function for an ARMA process, constructs the
autocovariances associated with the transformed process described
in equation (3.3.1) of _[1] in a memory efficient, and so fast, way.
Parameters
----------
ar : ndarray
Autoregressive coefficients, including the zero lag, where the sign
convention assumes the coefficients are part of the lag polynomial on
the left-hand-side of the ARMA definition (i.e. they have the opposite
sign from the usual econometrics convention in which the coefficients
are on the right-hand-side of the ARMA definition).
ma : ndarray
Moving average coefficients, including the zero lag, where the sign
convention assumes the coefficients are part of the lag polynomial on
the right-hand-side of the ARMA definition (i.e. they have the same
sign from the usual econometrics convention in which the coefficients
are on the right-hand-side of the ARMA definition).
arma_acovf : ndarray
The vector of autocovariances of the ARMA process.
Returns
-------
acovf : ndarray
A matrix containing the autocovariances of the portion of the
transformed process with time-varying autocovariances. Its dimension
is equal to `min(m * 2, n)` where `m = max(len(ar) - 1, len(ma) - 1)`
and `n` is the length of the input array `arma_acovf`. It is important
to note that only the values in the first `m` columns or `m` rows are
valid. In particular, the entries in the block `acovf[m:, m:]` should
not be used in any case (and in fact will always be equal to zeros).
acovf2 : ndarray
An array containing the autocovariance function of the portion of the
transformed process with time-invariant autocovariances. Its dimension
is equal to `max(n - m, 0)` where `n` is the length of the input
array `arma_acovf`.
Notes
-----
The definition of this autocovariance matrix is from _[1] equation 3.3.3.
This function assumes that the variance of the ARMA innovation term is
equal to one. If this is not true, then the calling program should replace
`arma_acovf` with `arma_acovf / sigma2`, where sigma2 is that variance.
This function is relatively fast even when `arma_acovf` is large, since
it only constructs the full autocovariance matrix for a generally small
subset of observations. The trade-off is that the output of this function
is somewhat more difficult to use.
References
----------
.. [1] Brockwell, Peter J., and Richard A. Davis. 2009.
Time Series: Theory and Methods. 2nd ed. 1991.
New York, NY: Springer.
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cexpl@@GLIBC_2.2.5 npy_cpowf npy_lcmull npy_copysignl npy_ceilf PyPyObject_GetAttr .symtab .strtab .shstrtab .note.gnu.build-id .gnu.hash .dynsym .dynstr .gnu.version .gnu.version_r .rela.dyn .rela.plt .init .plt.got .text .fini .rodata .eh_frame_hdr .eh_frame .init_array .fini_array .jcr .dynamic .got.plt .data .bss .comment È È $ . öÿÿo ð ð 8 h1 @ è: è: H ÿÿÿo lP lP U þÿÿo T T P d àT àT È1 n B ¨ ¨ ° x X£ X£ s p£ p£ 0 ~ ¶ ¶ °¶ °¶ E9 øï øï ð ð h; + + , © ¸7 ¸7 DO ³ è$ è ¿ ð$ ð Ë ø$ ø Ð $ à à$ à Ù $ ¨ â $ è `8% H8 Ð í 0 H8 - x8 (h I çc ö