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test_arima.py
2746 lines (2328 loc) · 105 KB
/
test_arima.py
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from statsmodels.compat.platform import (PLATFORM_OSX, PLATFORM_WIN,
PLATFORM_WIN32)
from statsmodels.compat.python import lrange
import os
import pickle
import warnings
from io import BytesIO
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_almost_equal, assert_allclose, assert_raises
from pandas import DatetimeIndex, date_range, period_range
import statsmodels.sandbox.tsa.fftarma as fa
from statsmodels.datasets.macrodata import load_pandas as load_macrodata_pandas
from statsmodels.regression.linear_model import OLS
from statsmodels.tools.sm_exceptions import (
ValueWarning, HessianInversionWarning, SpecificationWarning,
MissingDataError)
from statsmodels.tools.testing import assert_equal
from statsmodels.tsa.ar_model import AutoReg
from statsmodels.tsa.arima_model import ARMA, ARIMA
from statsmodels.tsa.arima_process import arma_generate_sample
from statsmodels.tsa.arma_mle import Arma
from statsmodels.tsa.tests.results import results_arma, results_arima
DECIMAL_4 = 4
DECIMAL_3 = 3
DECIMAL_2 = 2
DECIMAL_1 = 1
current_path = os.path.dirname(os.path.abspath(__file__))
ydata_path = os.path.join(current_path, 'results', 'y_arma_data.csv')
with open(ydata_path, "rb") as fd:
y_arma = np.genfromtxt(fd, delimiter=",", skip_header=1, dtype=float)
cpi_dates = period_range(start='1959q1', end='2009q3', freq='Q')
sun_dates = period_range(start='1700', end='2008', freq='A')
cpi_predict_dates = period_range(start='2009q3', end='2015q4', freq='Q')
sun_predict_dates = period_range(start='2008', end='2033', freq='A')
def test_compare_arma():
# this is a preliminary test to compare arma_kf, arma_cond_ls
# and arma_cond_mle
# the results returned by the fit methods are incomplete
# for now without random.seed
np.random.seed(9876565)
x = fa.ArmaFft([1, -0.5], [1., 0.4], 40).generate_sample(nsample=200,
burnin=1000)
modkf = ARMA(x, (1, 1))
reskf = modkf.fit(trend='nc', disp=-1)
dres = reskf
modc = Arma(x)
resls = modc.fit(order=(1, 1))
rescm = modc.fit_mle(order=(1, 1), start_params=[0.4, 0.4, 1.], disp=0)
# decimal 1 corresponds to threshold of 5% difference
# still different sign corrcted
assert_almost_equal(resls[0] / dres.params, np.ones(dres.params.shape),
decimal=1)
# rescm also contains variance estimate as last element of params
assert_almost_equal(rescm.params[:-1] / dres.params,
np.ones(dres.params.shape), decimal=1)
class CheckArmaResultsMixin(object):
"""
res2 are the results from gretl. They are in results/results_arma.
res1 are from statsmodels
"""
decimal_params = DECIMAL_4
def test_params(self):
assert_almost_equal(self.res1.params, self.res2.params,
self.decimal_params)
decimal_aic = DECIMAL_4
def test_aic(self):
assert_almost_equal(self.res1.aic, self.res2.aic, self.decimal_aic)
decimal_bic = DECIMAL_4
def test_bic(self):
assert_almost_equal(self.res1.bic, self.res2.bic, self.decimal_bic)
decimal_arroots = DECIMAL_4
def test_arroots(self):
assert_almost_equal(self.res1.arroots, self.res2.arroots,
self.decimal_arroots)
decimal_maroots = DECIMAL_4
def test_maroots(self):
assert_almost_equal(self.res1.maroots, self.res2.maroots,
self.decimal_maroots)
decimal_bse = DECIMAL_2
def test_bse(self):
assert_almost_equal(self.res1.bse, self.res2.bse, self.decimal_bse)
decimal_cov_params = DECIMAL_4
def test_covparams(self):
assert_almost_equal(self.res1.cov_params(), self.res2.cov_params,
self.decimal_cov_params)
decimal_hqic = DECIMAL_4
def test_hqic(self):
assert_almost_equal(self.res1.hqic, self.res2.hqic, self.decimal_hqic)
decimal_llf = DECIMAL_4
def test_llf(self):
assert_almost_equal(self.res1.llf, self.res2.llf, self.decimal_llf)
decimal_resid = DECIMAL_4
def test_resid(self):
assert_almost_equal(self.res1.resid, self.res2.resid,
self.decimal_resid)
decimal_fittedvalues = DECIMAL_4
def test_fittedvalues(self):
assert_almost_equal(self.res1.fittedvalues, self.res2.fittedvalues,
self.decimal_fittedvalues)
decimal_pvalues = DECIMAL_2
def test_pvalues(self):
assert_almost_equal(self.res1.pvalues, self.res2.pvalues,
self.decimal_pvalues)
decimal_t = DECIMAL_2 # only 2 decimal places in gretl output
def test_tvalues(self):
assert_almost_equal(self.res1.tvalues, self.res2.tvalues,
self.decimal_t)
decimal_sigma2 = DECIMAL_4
def test_sigma2(self):
assert_almost_equal(self.res1.sigma2, self.res2.sigma2,
self.decimal_sigma2)
@pytest.mark.smoke
def test_summary(self):
self.res1.summary()
@pytest.mark.smoke
def test_summary2(self):
self.res1.summary2()
class CheckForecastMixin(object):
decimal_forecast = DECIMAL_4
def test_forecast(self):
assert_almost_equal(self.res1.forecast_res, self.res2.forecast,
self.decimal_forecast)
decimal_forecasterr = DECIMAL_4
def test_forecasterr(self):
assert_almost_equal(self.res1.forecast_err, self.res2.forecasterr,
self.decimal_forecasterr)
class CheckDynamicForecastMixin(object):
decimal_forecast_dyn = 4
def test_dynamic_forecast(self):
assert_almost_equal(self.res1.forecast_res_dyn, self.res2.forecast_dyn,
self.decimal_forecast_dyn)
def test_forecasterr(self):
assert_almost_equal(self.res1.forecast_err_dyn,
self.res2.forecasterr_dyn,
DECIMAL_4)
class CheckArimaResultsMixin(CheckArmaResultsMixin):
def test_order(self):
assert self.res1.k_diff == self.res2.k_diff
assert self.res1.k_ar == self.res2.k_ar
assert self.res1.k_ma == self.res2.k_ma
decimal_predict_levels = DECIMAL_4
def test_predict_levels(self):
assert_almost_equal(self.res1.predict(typ='levels'), self.res2.linear,
self.decimal_predict_levels)
class Test_Y_ARMA11_NoConst(CheckArmaResultsMixin, CheckForecastMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 0]
cls.res1 = ARMA(endog, order=(1, 1)).fit(trend='nc', disp=-1)
(cls.res1.forecast_res, cls.res1.forecast_err,
confint) = cls.res1.forecast(10)
cls.res2 = results_arma.Y_arma11()
def test_pickle(self):
fh = BytesIO()
# test wrapped results load save pickle
self.res1.save(fh)
fh.seek(0, 0)
res_unpickled = self.res1.__class__.load(fh)
assert type(res_unpickled) is type(self.res1) # noqa: E721
class Test_Y_ARMA14_NoConst(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 1]
cls.res1 = ARMA(endog, order=(1, 4)).fit(trend='nc', disp=-1)
cls.res2 = results_arma.Y_arma14()
@pytest.mark.slow
class Test_Y_ARMA41_NoConst(CheckArmaResultsMixin, CheckForecastMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 2]
cls.res1 = ARMA(endog, order=(4, 1)).fit(trend='nc', disp=-1)
(cls.res1.forecast_res, cls.res1.forecast_err,
confint) = cls.res1.forecast(10)
cls.res2 = results_arma.Y_arma41()
cls.decimal_maroots = DECIMAL_3
class Test_Y_ARMA22_NoConst(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 3]
cls.res1 = ARMA(endog, order=(2, 2)).fit(trend='nc', disp=-1)
cls.res2 = results_arma.Y_arma22()
class Test_Y_ARMA50_NoConst(CheckArmaResultsMixin, CheckForecastMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 4]
cls.res1 = ARMA(endog, order=(5, 0)).fit(trend='nc', disp=-1)
(cls.res1.forecast_res, cls.res1.forecast_err,
confint) = cls.res1.forecast(10)
cls.res2 = results_arma.Y_arma50()
class Test_Y_ARMA02_NoConst(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 5]
cls.res1 = ARMA(endog, order=(0, 2)).fit(trend='nc', disp=-1)
cls.res2 = results_arma.Y_arma02()
class Test_Y_ARMA11_Const(CheckArmaResultsMixin, CheckForecastMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 6]
cls.res1 = ARMA(endog, order=(1, 1)).fit(trend="c", disp=-1)
(cls.res1.forecast_res, cls.res1.forecast_err,
confint) = cls.res1.forecast(10)
cls.res2 = results_arma.Y_arma11c()
class Test_Y_ARMA14_Const(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 7]
cls.res1 = ARMA(endog, order=(1, 4)).fit(trend="c", disp=-1)
cls.res2 = results_arma.Y_arma14c()
class Test_Y_ARMA41_Const(CheckArmaResultsMixin, CheckForecastMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 8]
cls.res2 = results_arma.Y_arma41c()
cls.res1 = ARMA(endog, order=(4, 1)).fit(trend="c", disp=-1,
start_params=cls.res2.params)
(cls.res1.forecast_res, cls.res1.forecast_err,
confint) = cls.res1.forecast(10)
cls.decimal_cov_params = DECIMAL_3
cls.decimal_fittedvalues = DECIMAL_3
cls.decimal_resid = DECIMAL_3
cls.decimal_params = DECIMAL_3
class Test_Y_ARMA22_Const(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 9]
cls.res1 = ARMA(endog, order=(2, 2)).fit(trend="c", disp=-1)
cls.res2 = results_arma.Y_arma22c()
def test_summary(self):
# regression test for html of roots table #4434
# we ignore whitespace in the assert
summ = self.res1.summary()
summ_roots = """\
<tableclass="simpletable">
<caption>Roots</caption>
<tr>
<td></td><th>Real</th><th>Imaginary</th><th>Modulus</th><th>Frequency</th>
</tr>
<tr>
<th>AR.1</th><td>1.0991</td><td>-1.2571j</td><td>1.6698</td><td>-0.1357</td>
</tr>
<tr>
<th>AR.2</th><td>1.0991</td><td>+1.2571j</td><td>1.6698</td><td>0.1357</td>
</tr>
<tr>
<th>MA.1</th><td>-1.1702</td><td>+0.0000j</td><td>1.1702</td><td>0.5000</td>
</tr>
<tr>
<th>MA.2</th><td>1.2215</td><td>+0.0000j</td><td>1.2215</td><td>0.0000</td>
</tr>
</table>"""
assert_equal(summ.tables[2]._repr_html_().replace(' ', ''),
summ_roots.replace(' ', ''))
class Test_Y_ARMA50_Const(CheckArmaResultsMixin, CheckForecastMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 10]
cls.res1 = ARMA(endog, order=(5, 0)).fit(trend="c", disp=-1)
(cls.res1.forecast_res, cls.res1.forecast_err,
confint) = cls.res1.forecast(10)
cls.res2 = results_arma.Y_arma50c()
class Test_Y_ARMA02_Const(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 11]
cls.res1 = ARMA(endog, order=(0, 2)).fit(trend="c", disp=-1)
cls.res2 = results_arma.Y_arma02c()
# cov_params and tvalues are off still but not as much vs. R
class Test_Y_ARMA11_NoConst_CSS(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 0]
cls.res1 = ARMA(endog, order=(1, 1)).fit(method="css", trend='nc',
disp=-1)
cls.res2 = results_arma.Y_arma11("css")
cls.decimal_t = DECIMAL_1
# better vs. R
class Test_Y_ARMA14_NoConst_CSS(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 1]
cls.res1 = ARMA(endog, order=(1, 4)).fit(method="css", trend='nc',
disp=-1)
cls.res2 = results_arma.Y_arma14("css")
cls.decimal_fittedvalues = DECIMAL_3
cls.decimal_resid = DECIMAL_3
cls.decimal_t = DECIMAL_1
# bse, etc. better vs. R
# maroot is off because maparams is off a bit (adjust tolerance?)
class Test_Y_ARMA41_NoConst_CSS(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 2]
cls.res1 = ARMA(endog, order=(4, 1)).fit(method="css", trend='nc',
disp=-1)
cls.res2 = results_arma.Y_arma41("css")
cls.decimal_t = DECIMAL_1
cls.decimal_pvalues = 0
cls.decimal_cov_params = DECIMAL_3
cls.decimal_maroots = DECIMAL_1
# same notes as above
class Test_Y_ARMA22_NoConst_CSS(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 3]
cls.res1 = ARMA(endog, order=(2, 2)).fit(method="css", trend='nc',
disp=-1)
cls.res2 = results_arma.Y_arma22("css")
cls.decimal_t = DECIMAL_1
cls.decimal_resid = DECIMAL_3
cls.decimal_pvalues = DECIMAL_1
cls.decimal_fittedvalues = DECIMAL_3
# NOTE: gretl just uses least squares for AR CSS
# so BIC, etc. is
# -2*res1.llf + np.log(nobs)*(res1.q+res1.p+res1.k)
# with no adjustment for p and no extra sigma estimate
# NOTE: so our tests use x-12 arima results which agree with us and are
# consistent with the rest of the models
class Test_Y_ARMA50_NoConst_CSS(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 4]
cls.res1 = ARMA(endog, order=(5, 0)).fit(method="css", trend='nc',
disp=-1)
cls.res2 = results_arma.Y_arma50("css")
cls.decimal_t = 0
cls.decimal_llf = DECIMAL_1 # looks like rounding error?
class Test_Y_ARMA02_NoConst_CSS(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 5]
cls.res1 = ARMA(endog, order=(0, 2)).fit(method="css", trend='nc',
disp=-1)
cls.res2 = results_arma.Y_arma02("css")
# NOTE: our results are close to --x-12-arima option and R
class Test_Y_ARMA11_Const_CSS(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 6]
cls.res1 = ARMA(endog, order=(1, 1)).fit(trend="c", method="css",
disp=-1)
cls.res2 = results_arma.Y_arma11c("css")
cls.decimal_params = DECIMAL_3
cls.decimal_cov_params = DECIMAL_3
cls.decimal_t = DECIMAL_1
class Test_Y_ARMA14_Const_CSS(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 7]
cls.res1 = ARMA(endog, order=(1, 4)).fit(trend="c", method="css",
disp=-1)
cls.res2 = results_arma.Y_arma14c("css")
cls.decimal_t = DECIMAL_1
cls.decimal_pvalues = DECIMAL_1
class Test_Y_ARMA41_Const_CSS(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 8]
cls.res1 = ARMA(endog, order=(4, 1)).fit(trend="c", method="css",
disp=-1)
cls.res2 = results_arma.Y_arma41c("css")
cls.decimal_t = DECIMAL_1
cls.decimal_cov_params = DECIMAL_1
cls.decimal_maroots = DECIMAL_3
cls.decimal_bse = DECIMAL_1
class Test_Y_ARMA22_Const_CSS(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 9]
cls.res1 = ARMA(endog, order=(2, 2)).fit(trend="c", method="css",
disp=-1)
cls.res2 = results_arma.Y_arma22c("css")
cls.decimal_t = 0
cls.decimal_pvalues = DECIMAL_1
class Test_Y_ARMA50_Const_CSS(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 10]
cls.res1 = ARMA(endog, order=(5, 0)).fit(trend="c", method="css",
disp=-1)
cls.res2 = results_arma.Y_arma50c("css")
cls.decimal_t = DECIMAL_1
cls.decimal_params = DECIMAL_3
cls.decimal_cov_params = DECIMAL_2
class Test_Y_ARMA02_Const_CSS(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 11]
cls.res1 = ARMA(endog, order=(0, 2)).fit(trend="c", method="css",
disp=-1)
cls.res2 = results_arma.Y_arma02c("css")
def test_reset_trend_error():
endog = y_arma[:, 0]
mod = ARMA(endog, order=(1, 1))
mod.fit(trend="c", disp=-1)
with pytest.raises(RuntimeError):
mod.fit(trend="nc", disp=-1)
@pytest.mark.slow
def test_start_params_bug():
data = np.array([1368., 1187, 1090, 1439, 2362, 2783, 2869, 2512, 1804,
1544, 1028, 869, 1737, 2055, 1947, 1618, 1196, 867, 997,
1862, 2525,
3250, 4023, 4018, 3585, 3004, 2500, 2441, 2749, 2466,
2157, 1847, 1463,
1146, 851, 993, 1448, 1719, 1709, 1455, 1950, 1763, 2075,
2343, 3570,
4690, 3700, 2339, 1679, 1466, 998, 853, 835, 922, 851,
1125, 1299, 1105,
860, 701, 689, 774, 582, 419, 846, 1132, 902, 1058, 1341,
1551, 1167,
975, 786, 759, 751, 649, 876, 720, 498, 553, 459, 543,
447, 415, 377,
373, 324, 320, 306, 259, 220, 342, 558, 825, 994, 1267,
1473, 1601,
1896, 1890, 2012, 2198, 2393, 2825, 3411, 3406, 2464,
2891, 3685, 3638,
3746, 3373, 3190, 2681, 2846, 4129, 5054, 5002, 4801,
4934, 4903, 4713,
4745, 4736, 4622, 4642, 4478, 4510, 4758, 4457, 4356,
4170, 4658, 4546,
4402, 4183, 3574, 2586, 3326, 3948, 3983, 3997, 4422,
4496, 4276, 3467,
2753, 2582, 2921, 2768, 2789, 2824, 2482, 2773, 3005,
3641, 3699, 3774,
3698, 3628, 3180, 3306, 2841, 2014, 1910, 2560, 2980,
3012, 3210, 3457,
3158, 3344, 3609, 3327, 2913, 2264, 2326, 2596, 2225,
1767, 1190, 792,
669, 589, 496, 354, 246, 250, 323, 495, 924, 1536, 2081,
2660, 2814, 2992,
3115, 2962, 2272, 2151, 1889, 1481, 955, 631, 288, 103,
60, 82, 107, 185,
618, 1526, 2046, 2348, 2584, 2600, 2515, 2345, 2351, 2355,
2409, 2449,
2645, 2918, 3187, 2888, 2610, 2740, 2526, 2383, 2936,
2968, 2635, 2617,
2790, 3906, 4018, 4797, 4919, 4942, 4656, 4444, 3898,
3908, 3678, 3605,
3186, 2139, 2002, 1559, 1235, 1183, 1096, 673, 389, 223,
352, 308, 365,
525, 779, 894, 901, 1025, 1047, 981, 902, 759, 569, 519,
408, 263, 156,
72, 49, 31, 41, 192, 423, 492, 552, 564, 723, 921, 1525,
2768, 3531, 3824,
3835, 4294, 4533, 4173, 4221, 4064, 4641, 4685, 4026,
4323, 4585, 4836,
4822, 4631, 4614, 4326, 4790, 4736, 4104, 5099, 5154,
5121, 5384, 5274,
5225, 4899, 5382, 5295, 5349, 4977, 4597, 4069, 3733,
3439, 3052, 2626,
1939, 1064, 713, 916, 832, 658, 817, 921, 772, 764, 824,
967, 1127, 1153,
824, 912, 957, 990, 1218, 1684, 2030, 2119, 2233, 2657,
2652, 2682, 2498,
2429, 2346, 2298, 2129, 1829, 1816, 1225, 1010, 748, 627,
469, 576, 532,
475, 582, 641, 605, 699, 680, 714, 670, 666, 636, 672,
679, 446, 248, 134,
160, 178, 286, 413, 676, 1025, 1159, 952, 1398, 1833,
2045, 2072, 1798,
1799, 1358, 727, 353, 347, 844, 1377, 1829, 2118, 2272,
2745, 4263, 4314,
4530, 4354, 4645, 4547, 5391, 4855, 4739, 4520, 4573,
4305, 4196, 3773,
3368, 2596, 2596, 2305, 2756, 3747, 4078, 3415, 2369,
2210, 2316, 2263,
2672, 3571, 4131, 4167, 4077, 3924, 3738, 3712, 3510,
3182, 3179, 2951,
2453, 2078, 1999, 2486, 2581, 1891, 1997, 1366, 1294,
1536, 2794, 3211,
3242, 3406, 3121, 2425, 2016, 1787, 1508, 1304, 1060,
1342, 1589, 2361,
3452, 2659, 2857, 3255, 3322, 2852, 2964, 3132, 3033,
2931, 2636, 2818,
3310, 3396, 3179, 3232, 3543, 3759, 3503, 3758, 3658,
3425, 3053, 2620,
1837, 923, 712, 1054, 1376, 1556, 1498, 1523, 1088, 728,
890, 1413, 2524,
3295, 4097, 3993, 4116, 3874, 4074, 4142, 3975, 3908,
3907, 3918, 3755,
3648, 3778, 4293, 4385, 4360, 4352, 4528, 4365, 3846,
4098, 3860, 3230,
2820, 2916, 3201, 3721, 3397, 3055, 2141, 1623, 1825,
1716, 2232, 2939,
3735, 4838, 4560, 4307, 4975, 5173, 4859, 5268, 4992,
5100, 5070, 5270,
4760, 5135, 5059, 4682, 4492, 4933, 4737, 4611, 4634,
4789, 4811, 4379,
4689, 4284, 4191, 3313, 2770, 2543, 3105, 2967, 2420,
1996, 2247, 2564,
2726, 3021, 3427, 3509, 3759, 3324, 2988, 2849, 2340,
2443, 2364, 1252,
623, 742, 867, 684, 488, 348, 241, 187, 279, 355, 423,
678, 1375, 1497,
1434, 2116, 2411, 1929, 1628, 1635, 1609, 1757, 2090,
2085, 1790, 1846,
2038, 2360, 2342, 2401, 2920, 3030, 3132, 4385, 5483,
5865, 5595, 5485,
5727, 5553, 5560, 5233, 5478, 5159, 5155, 5312, 5079,
4510, 4628, 4535,
3656, 3698, 3443, 3146, 2562, 2304, 2181, 2293, 1950,
1930, 2197, 2796,
3441, 3649, 3815, 2850, 4005, 5305, 5550, 5641, 4717,
5131, 2831, 3518,
3354, 3115, 3515, 3552, 3244, 3658, 4407, 4935, 4299,
3166, 3335, 2728,
2488, 2573, 2002, 1717, 1645, 1977, 2049, 2125, 2376,
2551, 2578, 2629,
2750, 3150, 3699, 4062, 3959, 3264, 2671, 2205, 2128,
2133, 2095, 1964,
2006, 2074, 2201, 2506, 2449, 2465, 2064, 1446, 1382, 983,
898, 489, 319,
383, 332, 276, 224, 144, 101, 232, 429, 597, 750, 908,
960, 1076, 951,
1062, 1183, 1404, 1391, 1419, 1497, 1267, 963, 682, 777,
906, 1149, 1439,
1600, 1876, 1885, 1962, 2280, 2711, 2591, 2411])
with warnings.catch_warnings():
warnings.simplefilter("ignore")
ARMA(data, order=(4, 1)).fit(start_ar_lags=5, disp=-1)
class Test_ARIMA101(CheckArmaResultsMixin):
@classmethod
def setup_class(cls):
endog = y_arma[:, 6]
cls.res1 = ARIMA(endog, (1, 0, 1)).fit(trend="c", disp=-1)
(cls.res1.forecast_res, cls.res1.forecast_err,
confint) = cls.res1.forecast(10)
cls.res2 = results_arma.Y_arma11c()
cls.res2.k_diff = 0
cls.res2.k_ar = 1
cls.res2.k_ma = 1
class Test_ARIMA111(CheckArimaResultsMixin, CheckForecastMixin,
CheckDynamicForecastMixin):
@classmethod
def setup_class(cls):
cpi = load_macrodata_pandas().data['cpi'].values
cls.res1 = ARIMA(cpi, (1, 1, 1)).fit(disp=-1)
cls.res2 = results_arima.ARIMA111()
# make sure endog names changes to D.cpi
cls.decimal_llf = 3
cls.decimal_aic = 3
cls.decimal_bic = 3
# TODO: why has dec_cov_params changed, used to be better
cls.decimal_cov_params = 2
cls.decimal_t = 0
(cls.res1.forecast_res,
cls.res1.forecast_err,
conf_int) = cls.res1.forecast(25)
# TODO: fix the indexing for the end here, I do not think this is right
# if we're going to treat it like indexing
# the forecast from 2005Q1 through 2009Q4 is indices
# 184 through 227 not 226
# note that the first one counts in the count so 164 + 64 is 65
# predictions
cls.res1.forecast_res_dyn = cls.res1.predict(start=164, end=164 + 63,
typ='levels',
dynamic=True)
def test_freq(self):
assert_almost_equal(self.res1.arfreq, [0.0000], 4)
assert_almost_equal(self.res1.mafreq, [0.0000], 4)
class Test_ARIMA111CSS(CheckArimaResultsMixin, CheckForecastMixin,
CheckDynamicForecastMixin):
@classmethod
def setup_class(cls):
cpi = load_macrodata_pandas().data['cpi'].values
cls.res1 = ARIMA(cpi, (1, 1, 1)).fit(disp=-1, method='css')
cls.res2 = results_arima.ARIMA111(method='css')
cls.res2.fittedvalues = - cpi[1:-1] + cls.res2.linear
# make sure endog names changes to D.cpi
(cls.res1.forecast_res,
cls.res1.forecast_err,
conf_int) = cls.res1.forecast(25)
cls.decimal_forecast = 2
cls.decimal_forecast_dyn = 2
cls.decimal_forecasterr = 3
cls.res1.forecast_res_dyn = cls.res1.predict(start=164, end=164 + 63,
typ='levels',
dynamic=True)
# precisions
cls.decimal_arroots = 3
cls.decimal_cov_params = 3
cls.decimal_hqic = 3
cls.decimal_maroots = 3
cls.decimal_t = 1
cls.decimal_fittedvalues = 2 # because of rounding when copying
cls.decimal_resid = 2
cls.decimal_predict_levels = DECIMAL_2
class Test_ARIMA112CSS(CheckArimaResultsMixin):
@classmethod
def setup_class(cls):
cpi = load_macrodata_pandas().data['cpi'].values
cls.res1 = ARIMA(cpi, (1, 1, 2)).fit(disp=-1, method='css',
start_params=[.905322, -.692425,
1.07366,
0.172024])
cls.res2 = results_arima.ARIMA112(method='css')
cls.res2.fittedvalues = - cpi[1:-1] + cls.res2.linear
# make sure endog names changes to D.cpi
cls.decimal_llf = 3
cls.decimal_aic = 3
cls.decimal_bic = 3
# TODO: fix the indexing for the end here, I do not think this is right
# if we're going to treat it like indexing
# the forecast from 2005Q1 through 2009Q4 is indices
# 184 through 227 not 226
# note that the first one counts in the count so 164 + 64 is 65
# predictions
# cls.res1.forecast_res_dyn = self.predict(start=164, end=164+63,
# typ='levels', dynamic=True)
# since we got from gretl do not have linear prediction in differences
cls.decimal_arroots = 3
cls.decimal_maroots = 2
cls.decimal_t = 1
cls.decimal_resid = 2
cls.decimal_fittedvalues = 3
cls.decimal_predict_levels = DECIMAL_3
def test_freq(self):
assert_almost_equal(self.res1.arfreq, [0.5000], 4)
assert_almost_equal(self.res1.mafreq, [0.5000, 0.5000], 4)
def test_arima_predict_mle_dates():
cpi = load_macrodata_pandas().data['cpi'].values
res1 = ARIMA(cpi, (4, 1, 1), dates=cpi_dates, freq='Q').fit(disp=-1)
file_path = os.path.join(current_path, 'results',
'results_arima_forecasts_all_mle.csv')
with open(file_path, "rb") as test_data:
arima_forecasts = np.genfromtxt(test_data, delimiter=",",
skip_header=1, dtype=float)
fc = arima_forecasts[:, 0]
fcdyn = arima_forecasts[:, 1]
fcdyn2 = arima_forecasts[:, 2]
start, end = 2, 51
fv = res1.predict('1959Q3', '1971Q4', typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_4)
assert_equal(res1.data.predict_dates, cpi_dates[start:end + 1])
start, end = 202, 227
fv = res1.predict('2009Q3', '2015Q4', typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_4)
assert_equal(res1.data.predict_dates, cpi_predict_dates)
# make sure dynamic works
start, end = '1960q2', '1971q4'
fv = res1.predict(start, end, dynamic=True, typ='levels')
assert_almost_equal(fv, fcdyn[5:51 + 1], DECIMAL_4)
start, end = '1965q1', '2015q4'
fv = res1.predict(start, end, dynamic=True, typ='levels')
assert_almost_equal(fv, fcdyn2[24:227 + 1], DECIMAL_4)
def test_arma_predict_mle_dates():
from statsmodels.datasets.sunspots import load_pandas
sunspots = load_pandas().data['SUNACTIVITY'].values
mod = ARMA(sunspots, (9, 0), dates=sun_dates, freq='A')
mod.method = 'mle'
assert_raises(ValueError, mod._get_prediction_index, '1701', '1751', True)
start, end = 2, 51
mod._get_prediction_index('1702', '1751', False)
assert_equal(mod.data.predict_dates, sun_dates[start:end + 1])
start, end = 308, 333
mod._get_prediction_index('2008', '2033', False)
assert_equal(mod.data.predict_dates, sun_predict_dates)
def test_arima_predict_css_dates():
cpi = load_macrodata_pandas().data['cpi'].values
res1 = ARIMA(cpi, (4, 1, 1), dates=cpi_dates, freq='Q').fit(disp=-1,
method='css',
trend='nc')
params = np.array([1.231272508473910,
-0.282516097759915,
0.170052755782440,
-0.118203728504945,
-0.938783134717947])
file_path = os.path.join(current_path, 'results',
'results_arima_forecasts_all_css.csv')
with open(file_path, "rb") as test_data:
arima_forecasts = np.genfromtxt(test_data, delimiter=",",
skip_header=1, dtype=float)
fc = arima_forecasts[:, 0]
fcdyn = arima_forecasts[:, 1]
fcdyn2 = arima_forecasts[:, 2]
start, end = 5, 51
fv = res1.model.predict(params, '1960Q2', '1971Q4', typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_4)
assert_equal(res1.data.predict_dates, cpi_dates[start:end + 1])
start, end = 202, 227
fv = res1.model.predict(params, '2009Q3', '2015Q4', typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_4)
assert_equal(res1.data.predict_dates, cpi_predict_dates)
# make sure dynamic works
start, end = 5, 51
fv = res1.model.predict(params, '1960Q2', '1971Q4', typ='levels',
dynamic=True)
assert_almost_equal(fv, fcdyn[start:end + 1], DECIMAL_4)
start, end = '1965q1', '2015q4'
fv = res1.model.predict(params, start, end, dynamic=True, typ='levels')
assert_almost_equal(fv, fcdyn2[24:227 + 1], DECIMAL_4)
def test_arma_predict_css_dates():
from statsmodels.datasets.sunspots import load_pandas
sunspots = load_pandas().data['SUNACTIVITY'].values
mod = ARMA(sunspots, (9, 0), dates=sun_dates, freq='A')
mod.method = 'css'
assert_raises(ValueError, mod._get_prediction_index, '1701', '1751', False)
def test_arima_predict_mle():
cpi = load_macrodata_pandas().data['cpi'].values
res1 = ARIMA(cpi, (4, 1, 1)).fit(disp=-1)
# fit the model so that we get correct endog length but use
file_path = os.path.join(current_path, 'results',
'results_arima_forecasts_all_mle.csv')
with open(file_path, "rb") as test_data:
arima_forecasts = np.genfromtxt(test_data, delimiter=",",
skip_header=1, dtype=float)
fc = arima_forecasts[:, 0]
fcdyn = arima_forecasts[:, 1]
fcdyn2 = arima_forecasts[:, 2]
fcdyn3 = arima_forecasts[:, 3]
fcdyn4 = arima_forecasts[:, 4]
# 0 indicates the first sample-observation below
# ie., the index after the pre-sample, these are also differenced once
# so the indices are moved back once from the cpi in levels
# start < p, end <p 1959q2 - 1959q4
start, end = 1, 3
fv = res1.predict(start, end, typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_4)
# start < p, end 0 1959q3 - 1960q1
start, end = 2, 4
fv = res1.predict(start, end, typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_4)
# start < p, end >0 1959q3 - 1971q4
start, end = 2, 51
fv = res1.predict(start, end, typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_4)
# start < p, end nobs 1959q3 - 2009q3
start, end = 2, 202
fv = res1.predict(start, end, typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_4)
# start < p, end >nobs 1959q3 - 2015q4
start, end = 2, 227
fv = res1.predict(start, end, typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_4)
# start 0, end >0 1960q1 - 1971q4
start, end = 4, 51
fv = res1.predict(start, end, typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_4)
# start 0, end nobs 1960q1 - 2009q3
start, end = 4, 202
fv = res1.predict(start, end, typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_4)
# start 0, end >nobs 1960q1 - 2015q4
start, end = 4, 227
fv = res1.predict(start, end, typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_4)
# start >p, end >0 1965q1 - 1971q4
start, end = 24, 51
fv = res1.predict(start, end, typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_4)
# start >p, end nobs 1965q1 - 2009q3
start, end = 24, 202
fv = res1.predict(start, end, typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_4)
# start >p, end >nobs 1965q1 - 2015q4
start, end = 24, 227
fv = res1.predict(start, end, typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_4)
# start nobs, end nobs 2009q3 - 2009q3
start, end = 202, 202
fv = res1.predict(start, end, typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_3)
# start nobs, end >nobs 2009q3 - 2015q4
start, end = 202, 227
fv = res1.predict(start, end, typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_3)
# start >nobs, end >nobs 2009q4 - 2015q4
start, end = 203, 227
fv = res1.predict(start, end, typ='levels')
assert_almost_equal(fv, fc[start:end + 1], DECIMAL_4)
# defaults
start, end = None, None
fv = res1.predict(start, end, typ='levels')
assert_almost_equal(fv, fc[1:203], DECIMAL_4)
# Dynamic
# start < p, end <p 1959q2 - 1959q4
start, end = 1, 3
with pytest.raises(ValueError, match='Start must be >= k_ar'):
fv = res1.predict(start, end, dynamic=True, typ='levels')
# start < p, end 0 1959q3 - 1960q1
start, end = 2, 4
with pytest.raises(ValueError, match='Start must be >= k_ar'):
res1.predict(start, end, dynamic=True, typ='levels')
# start < p, end >0 1959q3 - 1971q4
start, end = 2, 51
with pytest.raises(ValueError, match='Start must be >= k_ar'):
res1.predict(start, end, dynamic=True, typ='levels')
# start < p, end nobs 1959q3 - 2009q3
start, end = 2, 202
with pytest.raises(ValueError, match='Start must be >= k_ar'):
res1.predict(start, end, dynamic=True, typ='levels')
# start < p, end >nobs 1959q3 - 2015q4
start, end = 2, 227
with pytest.raises(ValueError, match='Start must be >= k_ar'):
res1.predict(start, end, dynamic=True, typ='levels')
# start 0, end >0 1960q1 - 1971q4
start, end = 5, 51
fv = res1.predict(start, end, dynamic=True, typ='levels')
assert_almost_equal(fv, fcdyn[start:end + 1], DECIMAL_4)
# start 0, end nobs 1960q1 - 2009q3
start, end = 5, 202
fv = res1.predict(start, end, dynamic=True, typ='levels')
assert_almost_equal(fv, fcdyn[start:end + 1], DECIMAL_4)
# start 0, end >nobs 1960q1 - 2015q4
start, end = 5, 227
fv = res1.predict(start, end, dynamic=True, typ='levels')
assert_almost_equal(fv, fcdyn[start:end + 1], DECIMAL_4)
# start >p, end >0 1965q1 - 1971q4
start, end = 24, 51
fv = res1.predict(start, end, dynamic=True, typ='levels')
assert_almost_equal(fv, fcdyn2[start:end + 1], DECIMAL_4)
# start >p, end nobs 1965q1 - 2009q3
start, end = 24, 202
fv = res1.predict(start, end, dynamic=True, typ='levels')
assert_almost_equal(fv, fcdyn2[start:end + 1], DECIMAL_4)
# start >p, end >nobs 1965q1 - 2015q4
start, end = 24, 227
fv = res1.predict(start, end, dynamic=True, typ='levels')
assert_almost_equal(fv, fcdyn2[start:end + 1], DECIMAL_4)
# start nobs, end nobs 2009q3 - 2009q3
start, end = 202, 202
fv = res1.predict(start, end, dynamic=True, typ='levels')
assert_almost_equal(fv, fcdyn3[start:end + 1], DECIMAL_4)
# start nobs, end >nobs 2009q3 - 2015q4
start, end = 202, 227
fv = res1.predict(start, end, dynamic=True, typ='levels')
assert_almost_equal(fv, fcdyn3[start:end + 1], DECIMAL_4)
# start >nobs, end >nobs 2009q4 - 2015q4
start, end = 203, 227
fv = res1.predict(start, end, dynamic=True, typ='levels')
assert_almost_equal(fv, fcdyn4[start:end + 1], DECIMAL_4)
# defaults
start, end = None, None
fv = res1.predict(start, end, dynamic=True, typ='levels')
assert_almost_equal(fv, fcdyn[5:203], DECIMAL_4)
def _check_start(model, given, expected, dynamic):
start, _, _, _ = model._get_prediction_index(given, None, dynamic)
assert_equal(start, expected)
def _check_end(model, given, end_expect, out_of_sample_expect):