def test_plot_quarter():
dta = sm.datasets.macrodata.load_pandas().data
dates = lmap('Q'.join, zip(dta.year.astype(int).apply(str),
dta.quarter.astype(int).apply(str)))
# test dates argument
quarter_plot(dta.unemp.values, dates)
plt.close('all')
# test with a DatetimeIndex with no freq
parser = pd.tseries.tools.parse_time_string
dta.set_index(pd.DatetimeIndex((x[0] for x in map(parser, dates))),
inplace=True)
quarter_plot(dta.unemp)
plt.close('all')
# w freq
# see pandas #6631
dta.index = pd.DatetimeIndex((x[0] for x in map(parser, dates)),
freq='QS-Oct')
quarter_plot(dta.unemp)
plt.close('all')
# w PeriodIndex
dta.index = pd.PeriodIndex((x[0] for x in map(parser, dates)),
freq='Q')
quarter_plot(dta.unemp)
plt.close('all')
def test_plot_quarter():
dta = sm.datasets.macrodata.load_pandas().data
dates = lmap(
'Q'.join,
zip(
dta.year.astype(int).apply(str),
dta.quarter.astype(int).apply(str)))
# test dates argument
quarter_plot(dta.unemp.values, dates)
plt.close('all')
# test with a DatetimeIndex with no freq
parser = pd.datetools.parse_time_string
dta.set_index(pd.DatetimeIndex((x[0] for x in map(parser, dates))),
inplace=True)
quarter_plot(dta.unemp)
plt.close('all')
# w freq
# see pandas #6631
dta.index = pd.DatetimeIndex((x[0] for x in map(parser, dates)),
freq='QS-Oct')
quarter_plot(dta.unemp)
plt.close('all')
# w PeriodIndex
dta.index = pd.PeriodIndex((x[0] for x in map(parser, dates)), freq='Q')
quarter_plot(dta.unemp)
plt.close('all')
def test_plot_quarter(close_figures):
dta = sm.datasets.macrodata.load_pandas().data
dates = lmap('Q'.join, zip(dta.year.astype(int).apply(str),
dta.quarter.astype(int).apply(str)))
# test dates argument
quarter_plot(dta.unemp.values, dates)
# test with a DatetimeIndex with no freq
dta.set_index(pd.to_datetime(dates), inplace=True)
quarter_plot(dta.unemp)
# w freq
# see pandas #6631
dta.index = pd.DatetimeIndex(pd.to_datetime(dates), freq='QS-Oct')
quarter_plot(dta.unemp)
# w PeriodIndex
dta.index = pd.PeriodIndex(pd.to_datetime(dates), freq='Q')
quarter_plot(dta.unemp)
def test_plot_quarter(close_figures):
dta = sm.datasets.macrodata.load_pandas().data
dates = lmap('Q'.join, zip(dta.year.astype(int).apply(str),
dta.quarter.astype(int).apply(str)))
# test dates argument
quarter_plot(dta.unemp.values, dates)
# test with a DatetimeIndex with no freq
dta.set_index(pd.to_datetime(dates), inplace=True)
quarter_plot(dta.unemp)
# w freq
# see pandas #6631
dta.index = pd.DatetimeIndex(pd.to_datetime(dates), freq='QS-Oct')
quarter_plot(dta.unemp)
# w PeriodIndex
dta.index = pd.PeriodIndex(pd.to_datetime(dates), freq='Q')
quarter_plot(dta.unemp)
from statsmodels.tsa.stattools import grangercausalitytests
grangercausalitytests(df3[['a', 'd']], maxlag=5)
grangercausalitytests(df3[['b', 'd']], maxlag=5)
np.random.seed(42)
df = pd.DataFrame(np.random.randint(20, 30, (50, 2)),
columns=['test', 'predictions'])
df.head()
df.plot(figsize=(12, 8))
from statsmodels.tools.eval_measures import mse, rmse, meanabs
mse(df['test'], df['predictions'])
rmse(df['test'], df['predictions'])
meanabs(df['test'], df['predictions'])
df1.head()
df1.index
from statsmodels.graphics.tsaplots import month_plot, quarter_plot
month_plot(df1['Pass_K'])
df1q = df1['Pass_K'].resample(rule='Q').sum()
quarter_plot(df1q)
print("Weak evidence against the null hypothesis - do not reject")
print("Data is NON-stationary")
print('*******************************')
# --- |||
# now run the test on the specific series
adf_run1 = adf_test(df1['Thousands of Passengers'])
print(adf_run1)
adf_run2 = adf_test(df2['Births'])
print(adf_run2)
#Granger causality test
#load a new dataset
df3 = pd.read_csv('data/samples.csv', index_col=0, parse_dates=True)
df3.index.freq='MS'
# run the test after inputing a guessed maxlag
granger1 = grangercausalitytests(df3[['a', 'd']], maxlag=2)
# seasonality check
month_plot(df1['Thousands of Passengers'])
#if we have quarterly data, we can resample the data
quarterly_data = df1['Thousands of Passengers'].resample(rule='Q').mean()
quarter_plot(quarterly_data)
# plt.show()
def plot_quarter(series, label=''):
quarter_plot(series)
# plt.title(label + ' Seasonal Subseries Plot')
plt.title('')
plt.xlabel('Quarter')
plt.ylabel(label)
from statsmodels.tools.eval_measures import mse, rmse, meanabs
mse(df['test'],df['predictions']) #: 17.02
df4 = pd.read_csv('airline_passengers.csv', index_col = 'Month',parse_dates= True )
df4.index.freq = 'MS'
from statsmodels.graphics.tsaplots import month_plot, quarter_plot
month_plot(df4['Thousands of Passengers']);
dfq = df4['Thousands of Passengers'].resample(rule = 'Q').mean()
quarter_plot(dfq)
dfout = pd.Series(dftest[0:4], index =['ADF Test Statisctics','p-value','#lags used','# Observations'] )
for key, val in dftest[4].items():
dfout[f'critical value ({key})'] = val
dftest1 = adfuller(df2['Births'])
