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scikits.statsmodels / statsmodels / tsa / tests / results / datamlw_tls.py
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import numpy as np
from numpy import array


class Holder(object):
    pass


mlpacf = Holder()
mlpacf.comment = 'mlab.parcorr(x, [], 2, nout=3)'
mlpacf.name = 'mlpacf'
mlpacf.lags1000 = array([[  0.],
       [  1.],
       [  2.],
       [  3.],
       [  4.],
       [  5.],
       [  6.],
       [  7.],
       [  8.],
       [  9.],
       [ 10.],
       [ 11.],
       [ 12.],
       [ 13.],
       [ 14.],
       [ 15.],
       [ 16.],
       [ 17.],
       [ 18.],
       [ 19.],
       [ 20.]])
mlpacf.bounds1000 = array([[ 0.06334064],
       [-0.06334064]])
mlpacf.lags100 = array([[  0.],
       [  1.],
       [  2.],
       [  3.],
       [  4.],
       [  5.],
       [  6.],
       [  7.],
       [  8.],
       [  9.],
       [ 10.],
       [ 11.],
       [ 12.],
       [ 13.],
       [ 14.],
       [ 15.],
       [ 16.],
       [ 17.],
       [ 18.],
       [ 19.],
       [ 20.]])
mlpacf.pacf100 = array([[ 1.        ],
       [ 0.47253777],
       [-0.49466966],
       [-0.02689319],
       [-0.00122204],
       [ 0.08419183],
       [ 0.03220774],
       [ 0.10404012],
       [ 0.05304617],
       [-0.04129564],
       [-0.04049451],
       [ 0.11727754],
       [ 0.11804158],
       [-0.05864957],
       [-0.15681802],
       [ 0.11828684],
       [ 0.05156002],
       [ 0.00694629],
       [ 0.01668964],
       [ 0.02236851],
       [-0.0909443 ]])
mlpacf.pacf1000 = array([[  1.00000000e+00],
       [  5.29288262e-01],
       [ -5.31849027e-01],
       [  1.17440051e-02],
       [ -5.37941905e-02],
       [ -4.11119348e-02],
       [ -2.40367432e-02],
       [  2.24289891e-02],
       [  3.33007235e-02],
       [  4.59658302e-02],
       [  6.65850553e-03],
       [ -3.76714278e-02],
       [  5.27229738e-02],
       [  2.50796558e-02],
       [ -4.42597301e-02],
       [ -1.95819186e-02],
       [  4.70451394e-02],
       [ -1.70963705e-03],
       [  3.04262524e-04],
       [ -6.22001614e-03],
       [ -1.16694989e-02]])
mlpacf.bounds100 = array([[ 0.20306923],
       [-0.20306923]])

mlacf = Holder()
mlacf.comment = 'mlab.autocorr(x, [], 2, nout=3)'
mlacf.name = 'mlacf'
mlacf.acf1000 = array([[ 1.        ],
       [ 0.5291635 ],
       [-0.10186759],
       [-0.35798372],
       [-0.25894203],
       [-0.06398397],
       [ 0.0513664 ],
       [ 0.08222289],
       [ 0.08115406],
       [ 0.07674254],
       [ 0.04540619],
       [-0.03024699],
       [-0.05886634],
       [-0.01422948],
       [ 0.01277825],
       [-0.01013384],
       [-0.00765693],
       [ 0.02183677],
       [ 0.03618889],
       [ 0.01622553],
       [-0.02073507]])
mlacf.lags1000 = array([[  0.],
       [  1.],
       [  2.],
       [  3.],
       [  4.],
       [  5.],
       [  6.],
       [  7.],
       [  8.],
       [  9.],
       [ 10.],
       [ 11.],
       [ 12.],
       [ 13.],
       [ 14.],
       [ 15.],
       [ 16.],
       [ 17.],
       [ 18.],
       [ 19.],
       [ 20.]])
mlacf.bounds1000 = array([[ 0.0795181],
       [-0.0795181]])
mlacf.lags100 = array([[  0.],
       [  1.],
       [  2.],
       [  3.],
       [  4.],
       [  5.],
       [  6.],
       [  7.],
       [  8.],
       [  9.],
       [ 10.],
       [ 11.],
       [ 12.],
       [ 13.],
       [ 14.],
       [ 15.],
       [ 16.],
       [ 17.],
       [ 18.],
       [ 19.],
       [ 20.]])
mlacf.bounds100 = array([[ 0.24319646],
       [-0.24319646]])
mlacf.acf100 = array([[ 1.        ],
       [ 0.47024791],
       [-0.1348087 ],
       [-0.32905777],
       [-0.18632437],
       [ 0.06223404],
       [ 0.16645194],
       [ 0.12589966],
       [ 0.04805397],
       [-0.03785273],
       [-0.0956997 ],
       [ 0.00644021],
       [ 0.17157144],
       [ 0.12370327],
       [-0.07597526],
       [-0.13865131],
       [ 0.02730275],
       [ 0.13624193],
       [ 0.10417949],
       [ 0.01114516],
       [-0.09727938]])

mlccf = Holder()
mlccf.comment = 'mlab.crosscorr(x[4:], x[:-4], [], 2, nout=3)'
mlccf.name = 'mlccf'
mlccf.ccf100 = array([[ 0.20745123],
       [ 0.12351939],
       [-0.03436893],
       [-0.14550879],
       [-0.10570855],
       [ 0.0108839 ],
       [ 0.1108941 ],
       [ 0.14562415],
       [ 0.02872607],
       [-0.14976649],
       [-0.08274954],
       [ 0.13158485],
       [ 0.18350343],
       [ 0.00633845],
       [-0.10359988],
       [-0.0416147 ],
       [ 0.05056298],
       [ 0.13438945],
       [ 0.17832125],
       [ 0.06665153],
       [-0.19999538],
       [-0.31700548],
       [-0.09727956],
       [ 0.46547234],
       [ 0.92934645],
       [ 0.44480271],
       [-0.09228691],
       [-0.21627289],
       [-0.05447732],
       [ 0.13786254],
       [ 0.15409039],
       [ 0.07466298],
       [-0.01000896],
       [-0.06744264],
       [-0.0607185 ],
       [ 0.04338471],
       [ 0.12336618],
       [ 0.07712367],
       [-0.08739259],
       [-0.09319212],
       [ 0.04426167]])
mlccf.lags1000 = array([[-20.],
       [-19.],
       [-18.],
       [-17.],
       [-16.],
       [-15.],
       [-14.],
       [-13.],
       [-12.],
       [-11.],
       [-10.],
       [ -9.],
       [ -8.],
       [ -7.],
       [ -6.],
       [ -5.],
       [ -4.],
       [ -3.],
       [ -2.],
       [ -1.],
       [  0.],
       [  1.],
       [  2.],
       [  3.],
       [  4.],
       [  5.],
       [  6.],
       [  7.],
       [  8.],
       [  9.],
       [ 10.],
       [ 11.],
       [ 12.],
       [ 13.],
       [ 14.],
       [ 15.],
       [ 16.],
       [ 17.],
       [ 18.],
       [ 19.],
       [ 20.]])
mlccf.bounds1000 = array([[ 0.06337243],
       [-0.06337243]])
mlccf.ccf1000 = array([[ 0.02733339],
       [ 0.04372407],
       [ 0.01082335],
       [-0.02755073],
       [-0.02076039],
       [ 0.01624263],
       [ 0.03622844],
       [ 0.02186092],
       [-0.00766506],
       [-0.0101448 ],
       [ 0.01279167],
       [-0.01424596],
       [-0.05893064],
       [-0.03028013],
       [ 0.04545462],
       [ 0.076825  ],
       [ 0.08124118],
       [ 0.08231121],
       [ 0.05142144],
       [-0.06405412],
       [-0.25922346],
       [-0.35806674],
       [-0.1017256 ],
       [ 0.5293535 ],
       [ 0.99891094],
       [ 0.52941977],
       [-0.10127572],
       [-0.35691466],
       [-0.25943369],
       [-0.06458511],
       [ 0.05026194],
       [ 0.08196501],
       [ 0.08242852],
       [ 0.07775845],
       [ 0.04590431],
       [-0.03195209],
       [-0.06162966],
       [-0.01395345],
       [ 0.01448736],
       [-0.00952503],
       [-0.00927344]])
mlccf.lags100 = array([[-20.],
       [-19.],
       [-18.],
       [-17.],
       [-16.],
       [-15.],
       [-14.],
       [-13.],
       [-12.],
       [-11.],
       [-10.],
       [ -9.],
       [ -8.],
       [ -7.],
       [ -6.],
       [ -5.],
       [ -4.],
       [ -3.],
       [ -2.],
       [ -1.],
       [  0.],
       [  1.],
       [  2.],
       [  3.],
       [  4.],
       [  5.],
       [  6.],
       [  7.],
       [  8.],
       [  9.],
       [ 10.],
       [ 11.],
       [ 12.],
       [ 13.],
       [ 14.],
       [ 15.],
       [ 16.],
       [ 17.],
       [ 18.],
       [ 19.],
       [ 20.]])
mlccf.bounds100 = array([[ 0.20412415],
       [-0.20412415]])

mlywar = Holder()
mlywar.comment = "mlab.ar(x100-x100.mean(), 10, 'yw').a.ravel()"
mlywar.arcoef100 = array([ 1.        , -0.66685531,  0.43519425, -0.00399862,  0.05521524,
       -0.09366752,  0.01093454, -0.00688404, -0.04739089,  0.00127931,
        0.03946846])
mlywar.arcoef1000 = array([ 1.        , -0.81230253,  0.55766432, -0.02370962,  0.02688963,
        0.01110911,  0.02239171, -0.01891209, -0.00240527, -0.01752532,
       -0.06348611,  0.0609686 , -0.00717163, -0.0467326 , -0.00122755,
        0.06004768, -0.04893984,  0.00575949,  0.00249315, -0.00560358,
        0.01248498])
mlywar.name = 'mlywar'