def test_invalid():
# Tests that invalid options raise errors
# (note that this is only invalid options specific to `ARIMA`, and not
# invalid options that would raise errors in SARIMAXSpecification).
endog = dta['infl'].iloc[:50]
mod = ARIMA(endog, order=(1, 0, 0))
# Need valid method
assert_raises(ValueError, mod.fit, method='not_a_method')
# Can only use 'statespace' with fixed parameters
with mod.fix_params({'ar.L1': 0.5}):
assert_raises(ValueError, mod.fit, method='yule_walker')
# Cannot override model-level values in fit
assert_raises(ValueError, mod.fit, method='statespace', method_kwargs={
'enforce_stationarity': False})
# start_params only valid for MLE methods
assert_raises(ValueError, mod.fit, method='yule_walker',
start_params=[0.5, 1.])
# has_exog and gls=False with non-statespace method
mod2 = ARIMA(endog, order=(1, 0, 0), trend='c')
assert_raises(ValueError, mod2.fit, method='yule_walker', gls=False)
# non-stationary parameters
mod3 = ARIMA(np.arange(100) * 1.0, order=(1, 0, 0), trend='n')
assert_raises(ValueError, mod3.fit, method='hannan_rissanen')
# non-invertible parameters
mod3 = ARIMA(np.arange(20) * 1.0, order=(0, 0, 1), trend='n')
assert_raises(ValueError, mod3.fit, method='hannan_rissanen')
def test_hannan_rissanen_with_fixed_params(ar_order, ma_order, fixed_params):
# Test for basic uses of Hannan-Rissanen estimation with fixed parameters
endog = dta['infl'].diff().iloc[1:101]
desired_p, _ = hannan_rissanen(endog,
ar_order=ar_order,
ma_order=ma_order,
demean=False,
fixed_params=fixed_params)
# no constant or trend (since constant or trend would imply GLS estimation)
mod = ARIMA(endog,
order=(ar_order, 0, ma_order),
trend='n',
enforce_stationarity=False,
enforce_invertibility=False)
with mod.fix_params(fixed_params):
res = mod.fit(method='hannan_rissanen')
assert_allclose(res.params, desired_p.params)
output['value']['Critical Value(10%)'] = t[4]['10%']
print(output) #这里ts1没有过,ADF检验,但是看时序图,较为平稳.有几个周期的方差较大怀疑是异方差
#做lm检验模型是显著的异方差,但是书中根据经验判断,适用arima模型.
#那么臆测一下,异方差由于历史的几个周期导致,因为数据比较历史.并且周期少
#那么可以认为适用arima,解决之后开始绘制自相关图和偏自相关图
c = acorr_lm(data['ts1'].dropna())
print(c)
# lag_acf = acf(data['ts1'].dropna(), nlags=10,fft=False)
# lag_pacf = pacf(data['ts1'].dropna(), nlags=10, method='ols')
# fig, axes = plt.subplots(1,2, figsize=(20,5))
# plot_acf(data['ts1'].dropna(), lags=10, ax=axes[0])
# plot_pacf(data['ts1'].dropna(), lags=10, ax=axes[1], method='ols')
# plt.show(block=True)
# 疏系数模型书中给出的是ARIMA((1,4),1,0)
# 但是我在看貌似ARIMA((1,4),1,1)更好些
# order_trend=arma_order_select_ic(data['ts1'].dropna())#这里由于异方差,可能没给出最好结果
# print(order_trend['bic_min_order'])
# python疏系数方法，对比了arima(4,1,0)和(4,1,1)后根据AIC和BIC使用(4,1,0)更好
result_trend = ARIMA(data['fertility'],
order=(4, 1, 0),
enforce_stationarity=False)
with result_trend.fix_params({'ar.L2': 0, 'ar.L3': 0}):
result_trend = result_trend.fit()
print(result_trend.param_names)
print(result_trend.forecast())
from statsmodels.tsa.arima.model import ARIMA
from math import sqrt
from scipy.stats import chi2
ibm = pd.read_csv('../data/m-ibm3dx2608.txt', delimiter='\s+')
vw = ibm['vwrtn']
m3 = ARIMA(vw, order=(3, 0, 0))
res = m3.fit()
print(res.summary())
print(
f"intercept phi(0): {(res.polynomial_ar[0] + res.polynomial_ar[1] + res.polynomial_ar[2] + res.polynomial_ar[3]) * vw.mean()}")
# standard error of residuals
print(f"standard error of residuals: {sqrt(res.params['sigma2'])}")
# box test
print("Box-Ljung:")
box = sm.stats.acorr_ljungbox(res.resid, lags=[12], return_df=True)
pv = 1 - chi2.cdf(box['lb_stat'], 9)
print(f"pv: {pv[0]}")
m3 = ARIMA(vw, order=(3, 0, 0), enforce_stationarity=False)
with m3.fix_params({'ar.L2': 0}):
res = m3.fit()
print(res.summary())
print(
f"intercept phi(0): {(res.polynomial_ar[0] + res.polynomial_ar[1] + res.polynomial_ar[2] + res.polynomial_ar[3]) * vw.mean()}")
print(f"standard error of residuals: {sqrt(res.params['sigma2'])}")
print("Box-Ljung:")
box = sm.stats.acorr_ljungbox(res.resid, lags=[12], return_df=True)
print(box)
pv = 1 - chi2.cdf(box['lb_stat'], 10)
print(f"pv: {pv[0]}")
