Esempio n. 1
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def acovf_explicit(ar, ma, nobs):
    '''add correlation of MA representation explicitely

    '''
    ir = arma_impulse_response(ar, ma)
    acovfexpl = [np.dot(ir[:nobs-t], ir[t:nobs]) for t in range(10)]
    return acovfexpl
Esempio n. 2
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def test_arma_impulse_response():
    arrep = arma_impulse_response(armarep.ma, armarep.ar, nobs=21)[1:]
    marep = arma_impulse_response(armarep.ar, armarep.ma, nobs=21)[1:]
    assert_array_almost_equal(armarep.marep.ravel(), marep, 14)
    #difference in sign convention to matlab for AR term
    assert_array_almost_equal(-armarep.arrep.ravel(), arrep, 14)
Esempio n. 3
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def test_fi():
    #test identity of ma and ar representation of fi lag polynomial
    n = 100
    mafromar = arma_impulse_response(lpol_fiar(0.4, n=n), [1], n)
    assert_array_almost_equal(mafromar, lpol_fima(0.4, n=n), 13)
Esempio n. 4
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from gwstatsmodels.tsa.arima_process import arma_generate_sample, arma_impulse_response
from gwstatsmodels.tsa.arima_process import arma_acovf, arma_acf, ARIMA
#from movstat import acf, acovf
#from gwstatsmodels.sandbox.tsa import acf, acovf, pacf
from gwstatsmodels.tsa.stattools import acf, acovf, pacf

ar = [1., -0.6]
#ar = [1., 0.]
ma = [1., 0.4]
#ma = [1., 0.4, 0.6]
#ma = [1., 0.]
mod = ''#'ma2'
x = arma_generate_sample(ar, ma, 5000)
x_acf = acf(x)[:10]
x_ir = arma_impulse_response(ar, ma)

#print x_acf[:10]
#print x_ir[:10]
#irc2 = np.correlate(x_ir,x_ir,'full')[len(x_ir)-1:]
#print irc2[:10]
#print irc2[:10]/irc2[0]
#print irc2[:10-1] / irc2[1:10]
#print x_acf[:10-1] / x_acf[1:10]

# detrend helper from matplotlib.mlab
def detrend(x, key=None):
    if key is None or key=='constant':
        return detrend_mean(x)
    elif key=='linear':
        return detrend_linear(x)
Esempio n. 5
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    >>> # verified for points [[5,10,20,25]] at 4 decimals with Bhardwaj, Swanson, Journal of Eonometrics 2006
    '''
    print lpol_fiar(0.4, n=20)
    print lpol_fima(-0.4, n=20)
    print np.sum((lpol_fima(-0.4, n=n)[1:] + riinv[1:])**2) #different signs
    print np.sum((lpol_fiar(0.4, n=n)[1:] - riinv[1:])**2) #corrected signs

    #test is now in gwstatsmodels.tsa.tests.test_arima_process
    from gwstatsmodels.tsa.tests.test_arima_process import test_fi
    test_fi()

    ar_true = [1, -0.4]
    ma_true = [1, 0.5]


    ar_desired = arma_impulse_response(ma_true, ar_true)
    ar_app, ma_app, res = ar2arma(ar_desired, 2,1, n=100, mse='ar', start=[0.1])
    print ar_app, ma_app
    ar_app, ma_app, res = ar2arma(ar_desired, 2,2, n=100, mse='ar', start=[-0.1, 0.1])
    print ar_app, ma_app
    ar_app, ma_app, res = ar2arma(ar_desired, 2,3, n=100, mse='ar')#, start = [-0.1, 0.1])
    print ar_app, ma_app

    slow = 1
    if slow:
        ar_desired = lpol_fiar(0.4, n=100)
        ar_app, ma_app, res = ar2arma(ar_desired, 3, 1, n=100, mse='ar')#, start = [-0.1, 0.1])
        print ar_app, ma_app
        ar_app, ma_app, res = ar2arma(ar_desired, 10, 10, n=100, mse='ar')#, start = [-0.1, 0.1])
        print ar_app, ma_app