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(size=200,
burnin=1000)
# this used kalman filter through descriptive
# d = ARMA(x)
# d.fit((1,1), trend='nc')
# dres = d.res
modkf = ARMA(x)
##rkf = mkf.fit((1,1))
##rkf.params
reskf = modkf.fit((1,1), 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(np.abs(resls[0] / d.params), np.ones(d.params.shape), decimal=1)
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(np.abs(rescm.params[:-1] / d.params), np.ones(d.params.shape), decimal=1)
assert_almost_equal(rescm.params[:-1] / dres.params, np.ones(dres.params.shape), decimal=1)
def setupClass(cls):
endog = y_arma[:, 2]
cls.res1 = ARMA(endog).fit(order=(4, 1), 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
def setupClass(cls):
endog = y_arma[:, 11]
cls.res1 = ARMA(endog).fit(order=(0, 2),
trend="c",
method="css",
disp=-1)
cls.res2 = results_arma.Y_arma02c("css")
def setupClass(cls):
endog = y_arma[:, 7]
cls.res1 = ARMA(endog).fit(order=(1, 4), trend="c", disp=-1)
cls.res2 = results_arma.Y_arma14c()
if fast_kalman:
cls.decimal_t = 0
cls.decimal_cov_params -= 1
def setupClass(cls):
endog = y_arma[:, 0]
cls.res1 = ARMA(endog).fit(order=(1, 1),
method="css",
trend='nc',
disp=-1)
cls.res2 = results_arma.Y_arma11("css")
cls.decimal_t = DECIMAL_1
def setupClass(cls):
endog = y_arma[:, 4]
cls.res1 = ARMA(endog).fit(order=(5, 0),
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?
def setupClass(cls):
endog = y_arma[:, 9]
cls.res1 = ARMA(endog).fit(order=(2, 2),
trend="c",
method="css",
disp=-1)
cls.res2 = results_arma.Y_arma22c("css")
cls.decimal_t = 0
cls.decimal_pvalues = DECIMAL_1
def setupClass(cls):
endog = y_arma[:, 10]
cls.res1 = ARMA(endog).fit(order=(5, 0),
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
def setupClass(cls):
endog = y_arma[:, 1]
cls.res1 = ARMA(endog).fit(order=(1, 4),
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
def setupClass(cls):
endog = y_arma[:, 8]
cls.res1 = ARMA(endog).fit(order=(4, 1),
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
def setupClass(cls):
endog = y_arma[:, 2]
cls.res1 = ARMA(endog).fit(order=(4, 1),
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
def setupClass(cls):
endog = y_arma[:, 8]
cls.res1 = ARMA(endog).fit(order=(4, 1), trend="c", disp=-1)
(cls.res1.forecast_res, cls.res1.forecast_err,
confint) = cls.res1.forecast(10)
cls.res2 = results_arma.Y_arma41c()
cls.decimal_cov_params = DECIMAL_3
cls.decimal_fittedvalues = DECIMAL_3
cls.decimal_resid = DECIMAL_3
cls.decimal_params = DECIMAL_3
if fast_kalman:
cls.decimal_cov_params -= 2
cls.decimal_bse -= 1
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(size=200,
burnin=1000)
# this used kalman filter through descriptive
# d = ARMA(x)
# d.fit((1,1), trend='nc')
# dres = d.res
modkf = ARMA(x)
##rkf = mkf.fit((1,1))
##rkf.params
reskf = modkf.fit((1, 1), 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(np.abs(resls[0] / d.params), np.ones(d.params.shape), decimal=1)
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(np.abs(rescm.params[:-1] / d.params), np.ones(d.params.shape), decimal=1)
assert_almost_equal(rescm.params[:-1] / dres.params,
np.ones(dres.params.shape),
decimal=1)
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
])
res = ARMA(data).fit(order=(4, 1), disp=-1)
def test_reset_trend():
endog = y_arma[:, 0]
mod = ARMA(endog)
res1 = mod.fit(order=(1, 1), trend="c", disp=-1)
res2 = mod.fit(order=(1, 1), trend="nc", disp=-1)
assert_equal(len(res1.params), len(res2.params) + 1)
def setupClass(cls):
endog = y_arma[:, 0]
cls.res1 = ARMA(endog).fit(order=(1, 1), 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_reset_trend():
endog = y_arma[:,0]
mod = ARMA(endog)
res1 = mod.fit(order=(1,1), trend="c", disp=-1)
res2 = mod.fit(order=(1,1), trend="nc", disp=-1)
assert_equal(len(res1.params), len(res2.params)+1)
def setupClass(cls):
endog = y_arma[:, 11]
cls.res1 = ARMA(endog).fit(order=(0, 2), trend="c", disp=-1)
cls.res2 = results_arma.Y_arma02c()
if fast_kalman:
cls.decimal_t -= 1
def setupClass(cls):
endog = y_arma[:, 10]
cls.res1 = ARMA(endog).fit(order=(5, 0), trend="c", disp=-1)
(cls.res1.forecast_res, cls.res1.forecast_err,
confint) = cls.res1.forecast(10)
cls.res2 = results_arma.Y_arma50c()
def setupClass(cls):
endog = y_arma[:, 3]
cls.res1 = ARMA(endog).fit(order=(2, 2), trend='nc', disp=-1)
cls.res2 = results_arma.Y_arma22()
if fast_kalman:
cls.decimal_t -= 1
