"""ARMA process and estimation with scipy.signal.lfilter
Notes
-----
* written without textbook, works but not sure about everything
briefly checked and it looks to be standard least squares, see below
* theoretical autocorrelation function of general ARMA
Done, relatively easy to guess solution, time consuming to get
theoretical test cases, example file contains explicit formulas for
acovf of MA(1), MA(2) and ARMA(1,1)
Properties:
Judge, ... (1985): The Theory and Practise of Econometrics
Author: josefpktd
License: BSD
"""
import warnings
from statsmodels.compat.pandas import Appender
import numpy as np
from scipy import linalg, optimize, signal
from statsmodels.tools.docstring import Docstring, remove_parameters
from statsmodels.tools.validation import array_like
__all__ = [
"arma_acf",
"arma_acovf",
"arma_generate_sample",
"arma_impulse_response",
"arma2ar",
"arma2ma",
"deconvolve",
"lpol2index",
"index2lpol",
]
NONSTATIONARY_ERROR = """\
The model's autoregressive parameters (ar) indicate that the process
is non-stationary. arma_acovf can only be used with stationary processes.
"""
[docs]def arma_generate_sample(
ar, ma, nsample, scale=1, distrvs=None, axis=0, burnin=0
):
"""
Simulate data from an ARMA.
Parameters
----------
ar : array_like
The coefficient for autoregressive lag polynomial, including zero lag.
ma : array_like
The coefficient for moving-average lag polynomial, including zero lag.
nsample : int or tuple of ints
If nsample is an integer, then this creates a 1d timeseries of
length size. If nsample is a tuple, creates a len(nsample)
dimensional time series where time is indexed along the input
variable ``axis``. All series are unless ``distrvs`` generates
dependent data.
scale : float
The standard deviation of noise.
distrvs : function, random number generator
A function that generates the random numbers, and takes ``size``
as argument. The default is np.random.standard_normal.
axis : int
See nsample for details.
burnin : int
Number of observation at the beginning of the sample to drop.
Used to reduce dependence on initial values.
Returns
-------
ndarray
Random sample(s) from an ARMA process.
Notes
-----
As mentioned above, both the AR and MA components should include the
coefficient on the zero-lag. This is typically 1. Further, due to the
conventions used in signal processing used in signal.lfilter vs.
conventions in statistics for ARMA processes, the AR parameters should
have the opposite sign of what you might expect. See the examples below.
Examples
--------
>>> import numpy as np
>>> np.random.seed(12345)
>>> arparams = np.array([.75, -.25])
>>> maparams = np.array([.65, .35])
>>> ar = np.r_[1, -arparams] # add zero-lag and negate
>>> ma = np.r_[1, maparams] # add zero-lag
>>> y = sm.tsa.arma_generate_sample(ar, ma, 250)
>>> model = sm.tsa.ARIMA(y, (2, 0, 2), trend='n').fit(disp=0)
>>> model.params
array([ 0.79044189, -0.23140636, 0.70072904, 0.40608028])
"""
distrvs = np.random.standard_normal if distrvs is None else distrvs
if np.ndim(nsample) == 0:
nsample = [nsample]
if burnin:
# handle burin time for nd arrays
# maybe there is a better trick in scipy.fft code
newsize = list(nsample)
newsize[axis] += burnin
newsize = tuple(newsize)
fslice = [slice(None)] * len(newsize)
fslice[axis] = slice(burnin, None, None)
fslice = tuple(fslice)
else:
newsize = tuple(nsample)
fslice = tuple([slice(None)] * np.ndim(newsize))
eta = scale * distrvs(size=newsize)
return signal.lfilter(ma, ar, eta, axis=axis)[fslice]
[docs]def arma_acovf(ar, ma, nobs=10, sigma2=1, dtype=None):
"""
Theoretical autocovariances of stationary ARMA processes
Parameters
----------
ar : array_like, 1d
The coefficients for autoregressive lag polynomial, including zero lag.
ma : array_like, 1d
The coefficients for moving-average lag polynomial, including zero lag.
nobs : int
The number of terms (lags plus zero lag) to include in returned acovf.
sigma2 : float
Variance of the innovation term.
Returns
-------
ndarray
The autocovariance of ARMA process given by ar, ma.
See Also
--------
arma_acf : Autocorrelation function for ARMA processes.
acovf : Sample autocovariance estimation.
References
----------
.. [*] Brockwell, Peter J., and Richard A. Davis. 2009. Time Series:
Theory and Methods. 2nd ed. 1991. New York, NY: Springer.
"""
if dtype is None:
dtype = np.common_type(np.array(ar), np.array(ma), np.array(sigma2))
p = len(ar) - 1
q = len(ma) - 1
m = max(p, q) + 1
if sigma2.real < 0:
raise ValueError("Must have positive innovation variance.")
# Short-circuit for trivial corner-case
if p == q == 0:
out = np.zeros(nobs, dtype=dtype)
out[0] = sigma2
return out
elif p > 0 and np.max(np.abs(np.roots(ar))) >= 1:
raise ValueError(NONSTATIONARY_ERROR)
# Get the moving average representation coefficients that we need
ma_coeffs = arma2ma(ar, ma, lags=m)
# Solve for the first m autocovariances via the linear system
# described by (BD, eq. 3.3.8)
A = np.zeros((m, m), dtype=dtype)
b = np.zeros((m, 1), dtype=dtype)
# We need a zero-right-padded version of ar params
tmp_ar = np.zeros(m, dtype=dtype)
tmp_ar[: p + 1] = ar
for k in range(m):
A[k, : (k + 1)] = tmp_ar[: (k + 1)][::-1]
A[k, 1 : m - k] += tmp_ar[(k + 1) : m]
b[k] = sigma2 * np.dot(ma[k : q + 1], ma_coeffs[: max((q + 1 - k), 0)])
acovf = np.zeros(max(nobs, m), dtype=dtype)
try:
acovf[:m] = np.linalg.solve(A, b)[:, 0]
except np.linalg.LinAlgError:
raise ValueError(NONSTATIONARY_ERROR)
# Iteratively apply (BD, eq. 3.3.9) to solve for remaining autocovariances
if nobs > m:
zi = signal.lfiltic([1], ar, acovf[:m:][::-1])
acovf[m:] = signal.lfilter(
[1], ar, np.zeros(nobs - m, dtype=dtype), zi=zi
)[0]
return acovf[:nobs]
[docs]def arma_acf(ar, ma, lags=10):
"""
Theoretical autocorrelation function of an ARMA process.
Parameters
----------
ar : array_like
Coefficients for autoregressive lag polynomial, including zero lag.
ma : array_like
Coefficients for moving-average lag polynomial, including zero lag.
lags : int
The number of terms (lags plus zero lag) to include in returned acf.
Returns
-------
ndarray
The autocorrelations of ARMA process given by ar and ma.
See Also
--------
arma_acovf : Autocovariances from ARMA processes.
acf : Sample autocorrelation function estimation.
acovf : Sample autocovariance function estimation.
"""
acovf = arma_acovf(ar, ma, lags)
return acovf / acovf[0]
[docs]def arma_pacf(ar, ma, lags=10):
"""
Theoretical partial autocorrelation function of an ARMA process.
Parameters
----------
ar : array_like, 1d
The coefficients for autoregressive lag polynomial, including zero lag.
ma : array_like, 1d
The coefficients for moving-average lag polynomial, including zero lag.
lags : int
The number of terms (lags plus zero lag) to include in returned pacf.
Returns
-------
ndarrray
The partial autocorrelation of ARMA process given by ar and ma.
Notes
-----
Solves yule-walker equation for each lag order up to nobs lags.
not tested/checked yet
"""
# TODO: Should use rank 1 inverse update
apacf = np.zeros(lags)
acov = arma_acf(ar, ma, lags=lags + 1)
apacf[0] = 1.0
for k in range(2, lags + 1):
r = acov[:k]
apacf[k - 1] = linalg.solve(linalg.toeplitz(r[:-1]), r[1:])[-1]
return apacf
[docs]def arma_periodogram(ar, ma, worN=None, whole=0):
"""
Periodogram for ARMA process given by lag-polynomials ar and ma.
Parameters
----------
ar : array_like
The autoregressive lag-polynomial with leading 1 and lhs sign.
ma : array_like
The moving average lag-polynomial with leading 1.
worN : {None, int}, optional
An option for scipy.signal.freqz (read "w or N").
If None, then compute at 512 frequencies around the unit circle.
If a single integer, the compute at that many frequencies.
Otherwise, compute the response at frequencies given in worN.
whole : {0,1}, optional
An options for scipy.signal.freqz/
Normally, frequencies are computed from 0 to pi (upper-half of
unit-circle. If whole is non-zero compute frequencies from 0 to 2*pi.
Returns
-------
w : ndarray
The frequencies.
sd : ndarray
The periodogram, also known as the spectral density.
Notes
-----
Normalization ?
This uses signal.freqz, which does not use fft. There is a fft version
somewhere.
"""
w, h = signal.freqz(ma, ar, worN=worN, whole=whole)
sd = np.abs(h) ** 2 / np.sqrt(2 * np.pi)
if np.any(np.isnan(h)):
# this happens with unit root or seasonal unit root'
import warnings
warnings.warn(
"Warning: nan in frequency response h, maybe a unit " "root",
RuntimeWarning,
)
return w, sd
[docs]def arma_impulse_response(ar, ma, leads=100):
"""
Compute the impulse response function (MA representation) for ARMA process.
Parameters
----------
ar : array_like, 1d
The auto regressive lag polynomial.
ma : array_like, 1d
The moving average lag polynomial.
leads : int
The number of observations to calculate.
Returns
-------
ndarray
The impulse response function with nobs elements.
Notes
-----
This is the same as finding the MA representation of an ARMA(p,q).
By reversing the role of ar and ma in the function arguments, the
returned result is the AR representation of an ARMA(p,q), i.e
ma_representation = arma_impulse_response(ar, ma, leads=100)
ar_representation = arma_impulse_response(ma, ar, leads=100)
Fully tested against matlab
Examples
--------
AR(1)
>>> arma_impulse_response([1.0, -0.8], [1.], leads=10)
array([ 1. , 0.8 , 0.64 , 0.512 , 0.4096 ,
0.32768 , 0.262144 , 0.2097152 , 0.16777216, 0.13421773])
this is the same as
>>> 0.8**np.arange(10)
array([ 1. , 0.8 , 0.64 , 0.512 , 0.4096 ,
0.32768 , 0.262144 , 0.2097152 , 0.16777216, 0.13421773])
MA(2)
>>> arma_impulse_response([1.0], [1., 0.5, 0.2], leads=10)
array([ 1. , 0.5, 0.2, 0. , 0. , 0. , 0. , 0. , 0. , 0. ])
ARMA(1,2)
>>> arma_impulse_response([1.0, -0.8], [1., 0.5, 0.2], leads=10)
array([ 1. , 1.3 , 1.24 , 0.992 , 0.7936 ,
0.63488 , 0.507904 , 0.4063232 , 0.32505856, 0.26004685])
"""
impulse = np.zeros(leads)
impulse[0] = 1.0
return signal.lfilter(ma, ar, impulse)
[docs]def arma2ma(ar, ma, lags=100):
"""
A finite-lag approximate MA representation of an ARMA process.
Parameters
----------
ar : ndarray
The auto regressive lag polynomial.
ma : ndarray
The moving average lag polynomial.
lags : int
The number of coefficients to calculate.
Returns
-------
ndarray
The coefficients of AR lag polynomial with nobs elements.
Notes
-----
Equivalent to ``arma_impulse_response(ma, ar, leads=100)``
"""
return arma_impulse_response(ar, ma, leads=lags)
[docs]def arma2ar(ar, ma, lags=100):
"""
A finite-lag AR approximation of an ARMA process.
Parameters
----------
ar : array_like
The auto regressive lag polynomial.
ma : array_like
The moving average lag polynomial.
lags : int
The number of coefficients to calculate.
Returns
-------
ndarray
The coefficients of AR lag polynomial with nobs elements.
Notes
-----
Equivalent to ``arma_impulse_response(ma, ar, leads=100)``
"""
return arma_impulse_response(ma, ar, leads=lags)
# moved from sandbox.tsa.try_fi
[docs]def ar2arma(ar_des, p, q, n=20, mse="ar", start=None):
"""
Find arma approximation to ar process.
This finds the ARMA(p,q) coefficients that minimize the integrated
squared difference between the impulse_response functions (MA
representation) of the AR and the ARMA process. This does not check
whether the MA lag polynomial of the ARMA process is invertible, neither
does it check the roots of the AR lag polynomial.
Parameters
----------
ar_des : array_like
The coefficients of original AR lag polynomial, including lag zero.
p : int
The length of desired AR lag polynomials.
q : int
The length of desired MA lag polynomials.
n : int
The number of terms of the impulse_response function to include in the
objective function for the approximation.
mse : str, 'ar'
Not used.
start : ndarray
Initial values to use when finding the approximation.
Returns
-------
ar_app : ndarray
The coefficients of the AR lag polynomials of the approximation.
ma_app : ndarray
The coefficients of the MA lag polynomials of the approximation.
res : tuple
The result of optimize.leastsq.
Notes
-----
Extension is possible if we want to match autocovariance instead
of impulse response function.
"""
# TODO: convert MA lag polynomial, ma_app, to be invertible, by mirroring
# TODO: roots outside the unit interval to ones that are inside. How to do
# TODO: this?
# p,q = pq
def msear_err(arma, ar_des):
ar, ma = np.r_[1, arma[: p - 1]], np.r_[1, arma[p - 1 :]]
ar_approx = arma_impulse_response(ma, ar, n)
return ar_des - ar_approx # ((ar - ar_approx)**2).sum()
if start is None:
arma0 = np.r_[-0.9 * np.ones(p - 1), np.zeros(q - 1)]
else:
arma0 = start
res = optimize.leastsq(msear_err, arma0, ar_des, maxfev=5000)
arma_app = np.atleast_1d(res[0])
ar_app = (np.r_[1, arma_app[: p - 1]],)
ma_app = np.r_[1, arma_app[p - 1 :]]
return ar_app, ma_app, res
_arma_docs = {"ar": arma2ar.__doc__, "ma": arma2ma.__doc__}
[docs]def lpol2index(ar):
"""
Remove zeros from lag polynomial
Parameters
----------
ar : array_like
coefficients of lag polynomial
Returns
-------
coeffs : ndarray
non-zero coefficients of lag polynomial
index : ndarray
index (lags) of lag polynomial with non-zero elements
"""
with warnings.catch_warnings():
warnings.simplefilter("ignore", np.ComplexWarning)
ar = array_like(ar, "ar")
index = np.nonzero(ar)[0]
coeffs = ar[index]
return coeffs, index
[docs]def index2lpol(coeffs, index):
"""
Expand coefficients to lag poly
Parameters
----------
coeffs : ndarray
non-zero coefficients of lag polynomial
index : ndarray
index (lags) of lag polynomial with non-zero elements
Returns
-------
ar : array_like
coefficients of lag polynomial
"""
n = max(index)
ar = np.zeros(n + 1)
ar[index] = coeffs
return ar
[docs]def lpol_fima(d, n=20):
"""MA representation of fractional integration
.. math:: (1-L)^{-d} for |d|<0.5 or |d|<1 (?)
Parameters
----------
d : float
fractional power
n : int
number of terms to calculate, including lag zero
Returns
-------
ma : ndarray
coefficients of lag polynomial
"""
# hide import inside function until we use this heavily
from scipy.special import gammaln
j = np.arange(n)
return np.exp(gammaln(d + j) - gammaln(j + 1) - gammaln(d))
# moved from sandbox.tsa.try_fi
[docs]def lpol_fiar(d, n=20):
"""AR representation of fractional integration
.. math:: (1-L)^{d} for |d|<0.5 or |d|<1 (?)
Parameters
----------
d : float
fractional power
n : int
number of terms to calculate, including lag zero
Returns
-------
ar : ndarray
coefficients of lag polynomial
Notes:
first coefficient is 1, negative signs except for first term,
ar(L)*x_t
"""
# hide import inside function until we use this heavily
from scipy.special import gammaln
j = np.arange(n)
ar = -np.exp(gammaln(-d + j) - gammaln(j + 1) - gammaln(-d))
ar[0] = 1
return ar
# moved from sandbox.tsa.try_fi
[docs]def lpol_sdiff(s):
"""return coefficients for seasonal difference (1-L^s)
just a trivial convenience function
Parameters
----------
s : int
number of periods in season
Returns
-------
sdiff : list, length s+1
"""
return [1] + [0] * (s - 1) + [-1]
[docs]def deconvolve(num, den, n=None):
"""Deconvolves divisor out of signal, division of polynomials for n terms
calculates den^{-1} * num
Parameters
----------
num : array_like
signal or lag polynomial
denom : array_like
coefficients of lag polynomial (linear filter)
n : None or int
number of terms of quotient
Returns
-------
quot : ndarray
quotient or filtered series
rem : ndarray
remainder
Notes
-----
If num is a time series, then this applies the linear filter den^{-1}.
If both num and den are both lag polynomials, then this calculates the
quotient polynomial for n terms and also returns the remainder.
This is copied from scipy.signal.signaltools and added n as optional
parameter.
"""
num = np.atleast_1d(num)
den = np.atleast_1d(den)
N = len(num)
D = len(den)
if D > N and n is None:
quot = []
rem = num
else:
if n is None:
n = N - D + 1
input = np.zeros(n, float)
input[0] = 1
quot = signal.lfilter(num, den, input)
num_approx = signal.convolve(den, quot, mode="full")
if len(num) < len(num_approx): # 1d only ?
num = np.concatenate((num, np.zeros(len(num_approx) - len(num))))
rem = num - num_approx
return quot, rem
_generate_sample_doc = Docstring(arma_generate_sample.__doc__)
_generate_sample_doc.remove_parameters(["ar", "ma"])
_generate_sample_doc.replace_block("Notes", [])
_generate_sample_doc.replace_block("Examples", [])
[docs]class ArmaProcess(object):
r"""
Theoretical properties of an ARMA process for specified lag-polynomials.
Parameters
----------
ar : array_like
Coefficient for autoregressive lag polynomial, including zero lag.
Must be entered using the signs from the lag polynomial representation.
See the notes for more information about the sign.
ma : array_like
Coefficient for moving-average lag polynomial, including zero lag.
nobs : int, optional
Length of simulated time series. Used, for example, if a sample is
generated. See example.
Notes
-----
Both the AR and MA components must include the coefficient on the
zero-lag. In almost all cases these values should be 1. Further, due to
using the lag-polynomial representation, the AR parameters should
have the opposite sign of what one would write in the ARMA representation.
See the examples below.
The ARMA(p,q) process is described by
.. math::
y_{t}=\phi_{1}y_{t-1}+\ldots+\phi_{p}y_{t-p}+\theta_{1}\epsilon_{t-1}
+\ldots+\theta_{q}\epsilon_{t-q}+\epsilon_{t}
and the parameterization used in this function uses the lag-polynomial
representation,
.. math::
\left(1-\phi_{1}L-\ldots-\phi_{p}L^{p}\right)y_{t} =
\left(1+\theta_{1}L+\ldots+\theta_{q}L^{q}\right)\epsilon_{t}
Examples
--------
ARMA(2,2) with AR coefficients 0.75 and -0.25, and MA coefficients 0.65 and 0.35
>>> import statsmodels.api as sm
>>> import numpy as np
>>> np.random.seed(12345)
>>> arparams = np.array([.75, -.25])
>>> maparams = np.array([.65, .35])
>>> ar = np.r_[1, -arparams] # add zero-lag and negate
>>> ma = np.r_[1, maparams] # add zero-lag
>>> arma_process = sm.tsa.ArmaProcess(ar, ma)
>>> arma_process.isstationary
True
>>> arma_process.isinvertible
True
>>> arma_process.arroots
array([1.5-1.32287566j, 1.5+1.32287566j])
>>> y = arma_process.generate_sample(250)
>>> model = sm.tsa.ARIMA(y, (2, 0, 2), trend='n').fit(disp=0)
>>> model.params
array([ 0.79044189, -0.23140636, 0.70072904, 0.40608028])
The same ARMA(2,2) Using the from_coeffs class method
>>> arma_process = sm.tsa.ArmaProcess.from_coeffs(arparams, maparams)
>>> arma_process.arroots
array([1.5-1.32287566j, 1.5+1.32287566j])
"""
# TODO: Check unit root behavior
def __init__(self, ar=None, ma=None, nobs=100):
if ar is None:
ar = np.array([1.0])
if ma is None:
ma = np.array([1.0])
with warnings.catch_warnings():
warnings.simplefilter("ignore", np.ComplexWarning)
self.ar = array_like(ar, "ar")
self.ma = array_like(ma, "ma")
self.arcoefs = -self.ar[1:]
self.macoefs = self.ma[1:]
self.arpoly = np.polynomial.Polynomial(self.ar)
self.mapoly = np.polynomial.Polynomial(self.ma)
self.nobs = nobs
[docs] @classmethod
def from_roots(cls, maroots=None, arroots=None, nobs=100):
"""
Create ArmaProcess from AR and MA polynomial roots.
Parameters
----------
maroots : array_like
Roots for the MA polynomial
1 + theta_1*z + theta_2*z^2 + ..... + theta_n*z^n
arroots : array_like
Roots for the AR polynomial
1 - phi_1*z - phi_2*z^2 - ..... - phi_n*z^n
nobs : int, optional
Length of simulated time series. Used, for example, if a sample
is generated.
Returns
-------
ArmaProcess
Class instance initialized with arcoefs and macoefs.
Examples
--------
>>> arroots = [.75, -.25]
>>> maroots = [.65, .35]
>>> arma_process = sm.tsa.ArmaProcess.from_roots(arroots, maroots)
>>> arma_process.isstationary
True
>>> arma_process.isinvertible
True
"""
arpoly = np.polynomial.polynomial.Polynomial.fromroots(arroots)
mapoly = np.polynomial.polynomial.Polynomial.fromroots(maroots)
# As from_coeffs will create a polynomial with constant 1/-1,(MA/AR)
# we need to scale the polynomial coefficients accordingly
return cls(np.r_[1, -np.asarray(-1 * arpoly.coef[1:] / arpoly.coef[0])],
np.r_[1, np.asarray(mapoly.coef[1:] / mapoly.coef[0])],
nobs=nobs)
[docs] @classmethod
def from_coeffs(cls, arcoefs=None, macoefs=None, nobs=100):
"""
Create ArmaProcess from an ARMA representation.
Parameters
----------
arcoefs : array_like
Coefficient for autoregressive lag polynomial, not including zero
lag. The sign is inverted to conform to the usual time series
representation of an ARMA process in statistics. See the class
docstring for more information.
macoefs : array_like
Coefficient for moving-average lag polynomial, excluding zero lag.
nobs : int, optional
Length of simulated time series. Used, for example, if a sample
is generated.
Returns
-------
ArmaProcess
Class instance initialized with arcoefs and macoefs.
Examples
--------
>>> arparams = [.75, -.25]
>>> maparams = [.65, .35]
>>> arma_process = sm.tsa.ArmaProcess.from_coeffs(ar, ma)
>>> arma_process.isstationary
True
>>> arma_process.isinvertible
True
"""
arcoefs = [] if arcoefs is None else arcoefs
macoefs = [] if macoefs is None else macoefs
return cls(
np.r_[1, -np.asarray(arcoefs)],
np.r_[1, np.asarray(macoefs)],
nobs=nobs,
)
[docs] @classmethod
def from_estimation(cls, model_results, nobs=None):
"""
Create an ArmaProcess from the results of an ARIMA estimation.
Parameters
----------
model_results : ARIMAResults instance
A fitted model.
nobs : int, optional
If None, nobs is taken from the results.
Returns
-------
ArmaProcess
Class instance initialized from model_results.
See Also
--------
statsmodels.tsa.arima.model.ARIMA
The models class used to create the ArmaProcess
"""
nobs = nobs or model_results.nobs
return cls(
model_results.polynomial_reduced_ar,
model_results.polynomial_reduced_ma,
nobs=nobs,
)
def __mul__(self, oth):
if isinstance(oth, self.__class__):
ar = (self.arpoly * oth.arpoly).coef
ma = (self.mapoly * oth.mapoly).coef
else:
try:
aroth, maoth = oth
arpolyoth = np.polynomial.Polynomial(aroth)
mapolyoth = np.polynomial.Polynomial(maoth)
ar = (self.arpoly * arpolyoth).coef
ma = (self.mapoly * mapolyoth).coef
except:
raise TypeError("Other type is not a valid type")
return self.__class__(ar, ma, nobs=self.nobs)
def __repr__(self):
msg = "ArmaProcess({0}, {1}, nobs={2}) at {3}"
return msg.format(
self.ar.tolist(), self.ma.tolist(), self.nobs, hex(id(self))
)
def __str__(self):
return "ArmaProcess\nAR: {0}\nMA: {1}".format(
self.ar.tolist(), self.ma.tolist()
)
[docs] @Appender(remove_parameters(arma_acovf.__doc__, ["ar", "ma", "sigma2"]))
def acovf(self, nobs=None):
nobs = nobs or self.nobs
return arma_acovf(self.ar, self.ma, nobs=nobs)
[docs] @Appender(remove_parameters(arma_acf.__doc__, ["ar", "ma"]))
def acf(self, lags=None):
lags = lags or self.nobs
return arma_acf(self.ar, self.ma, lags=lags)
[docs] @Appender(remove_parameters(arma_pacf.__doc__, ["ar", "ma"]))
def pacf(self, lags=None):
lags = lags or self.nobs
return arma_pacf(self.ar, self.ma, lags=lags)
[docs] @Appender(
remove_parameters(
arma_periodogram.__doc__, ["ar", "ma", "worN", "whole"]
)
)
def periodogram(self, nobs=None):
nobs = nobs or self.nobs
return arma_periodogram(self.ar, self.ma, worN=nobs)
[docs] @Appender(remove_parameters(arma_impulse_response.__doc__, ["ar", "ma"]))
def impulse_response(self, leads=None):
leads = leads or self.nobs
return arma_impulse_response(self.ar, self.ma, leads=leads)
[docs] @Appender(remove_parameters(arma2ma.__doc__, ["ar", "ma"]))
def arma2ma(self, lags=None):
lags = lags or self.lags
return arma2ma(self.ar, self.ma, lags=lags)
[docs] @Appender(remove_parameters(arma2ar.__doc__, ["ar", "ma"]))
def arma2ar(self, lags=None):
lags = lags or self.lags
return arma2ar(self.ar, self.ma, lags=lags)
@property
def arroots(self):
"""Roots of autoregressive lag-polynomial"""
return self.arpoly.roots()
@property
def maroots(self):
"""Roots of moving average lag-polynomial"""
return self.mapoly.roots()
@property
def isstationary(self):
"""
Arma process is stationary if AR roots are outside unit circle.
Returns
-------
bool
True if autoregressive roots are outside unit circle.
"""
if np.all(np.abs(self.arroots) > 1.0):
return True
else:
return False
@property
def isinvertible(self):
"""
Arma process is invertible if MA roots are outside unit circle.
Returns
-------
bool
True if moving average roots are outside unit circle.
"""
if np.all(np.abs(self.maroots) > 1):
return True
else:
return False
[docs] def invertroots(self, retnew=False):
"""
Make MA polynomial invertible by inverting roots inside unit circle.
Parameters
----------
retnew : bool
If False (default), then return the lag-polynomial as array.
If True, then return a new instance with invertible MA-polynomial.
Returns
-------
manew : ndarray
A new invertible MA lag-polynomial, returned if retnew is false.
wasinvertible : bool
True if the MA lag-polynomial was already invertible, returned if
retnew is false.
armaprocess : new instance of class
If retnew is true, then return a new instance with invertible
MA-polynomial.
"""
# TODO: variable returns like this?
pr = self.maroots
mainv = self.ma
invertible = self.isinvertible
if not invertible:
pr[np.abs(pr) < 1] = 1.0 / pr[np.abs(pr) < 1]
pnew = np.polynomial.Polynomial.fromroots(pr)
mainv = pnew.coef / pnew.coef[0]
if retnew:
return self.__class__(self.ar, mainv, nobs=self.nobs)
else:
return mainv, invertible
[docs] @Appender(str(_generate_sample_doc))
def generate_sample(
self, nsample=100, scale=1.0, distrvs=None, axis=0, burnin=0
):
return arma_generate_sample(
self.ar, self.ma, nsample, scale, distrvs, axis=axis, burnin=burnin
)