def test_trends_low_memory(trend):
rnd = np.random.RandomState(12345)
y = np.cumsum(rnd.randn(250))
adf = ADF(y, trend=trend, max_lags=16)
adf2 = ADF(y, trend=trend, low_memory=True, max_lags=16)
assert adf.lags == adf2.lags
assert adf.max_lags == 16
adf.max_lags = 1
assert_equal(adf.lags, 1)
assert_equal(adf.max_lags, 1)
def test_representations(trend):
rnd = np.random.RandomState(12345)
y = np.cumsum(rnd.randn(250))
adf = ADF(y, trend=trend, max_lags=16)
check = 'Constant'
if trend == 'nc':
check = 'No Trend'
assert check in adf.__repr__()
assert check in adf.__repr__()
assert check in adf._repr_html_()
assert 'class="simpletable"' in adf._repr_html_()
def test_adf_auto_t_stat(self):
adf = ADF(self.inflation, method='t-stat')
assert_equal(adf.lags, 10)
old_stat = adf.stat
adf.lags += 1
assert adf.stat != old_stat
old_stat = adf.stat
assert_equal(adf.y, self.inflation)
adf.trend = 'ctt'
assert adf.stat != old_stat
assert adf.trend == 'ctt'
assert len(adf.valid_trends) == len(('nc', 'c', 'ct', 'ctt'))
for d in adf.valid_trends:
assert d in ('nc', 'c', 'ct', 'ctt')
assert adf.null_hypothesis == 'The process contains a unit root.'
assert adf.alternative_hypothesis == 'The process is weakly stationary.'
def test_adf_lags_10(self):
adf = ADF(self.inflation, lags=10)
assert_almost_equal(adf.stat, -2.28375, DECIMAL_4)
adf.summary()
def test_invalid_determinstic(self):
adf = ADF(self.inflation)
with pytest.raises(ValueError):
adf.trend = 'bad-value'
def test_negative_lag(self):
adf = ADF(self.inflation)
with pytest.raises(ValueError):
adf.lags = -1
#查看前3行数据 CPI.head(n=3) #查看后3行数据 CPI.tail(n=3) CPI.shape #剔除最后3期数据,构造用于建模的数据子集 CPItrain = CPI[3:] CPItrain.head(n=3) #绘制时序图，直观了解数据情况 CPI.sort_index().plot(title='CPI 2001-2014') #进行ADF单位根检验，并查看结果； CPItrain = CPItrain.dropna().CPI print(ADF(CPItrain, max_lags=10).summary().as_text()) #lag即为上述检验表达式中的m，在这里我们选择检验12阶的自相关系数。 LjungBox = stattools.q_stat(stattools.acf(CPItrain)[1:12], len(CPItrain)) LjungBox[1][-1] #将画面一分为二 axe1 = plt.subplot(121) axe2 = plt.subplot(122) #在第一个画面中画出序列的自相关系数图 plot1 = plot_acf(CPItrain, lags=30, ax=axe1) #在第二个画面中画出序列的偏自相关系数图 plot2 = plot_pacf(CPItrain, lags=30, ax=axe2) #order表示建立的模型的阶数，c(1,0,1)表示建立的是ARMA(1,1)模型； #中间的数字0表示使用原始的、未进行过差分（差分次数为0）的数据； #此处我们无需考虑它
import numpy as np
import pandas as pd
import pandas_datareader as web
import matplotlib.pyplot as plt
import datetime
start = datetime.datetime(2018, 1, 1)
end = datetime.datetime(2019, 6, 30)
apple = web.DataReader('AAPL', 'yahoo', start, end)
apple = apple.dropna()
apple = pd.DataFrame(apple['Adj Close'].values,
index=pd.to_datetime(apple.index),
columns=['Price'])
apple = np.log(apple / apple.shift(1))
apple = apple.dropna()
apple.columns = ['Return']
apple.tail(3)
traindata = apple[:-3]
apple.plot()
from arch.unitroot import ADF
result = ADF(traindata.Return, max_lags=10)
print(result.summary().as_text())
from statsmodels.tsa import stattools
LjungBox = stattools.q_stat(stattools.acf(traindata)[1:12], len(traindata))
LjungBox[1][-1]
import statsmodels.graphics.tsaplots as ts
ts.plot_acf(traindata, use_vlines=True, lags=30)
