axes[2].set_ylabel('Trend')
self.irregular.plot(ax=axes[3], legend=False)
axes[3].set_ylabel('Irregular')
fig.tight_layout()
return fig
if __name__ == "__main__":
import numpy as np
from statsmodels.tsa.arima_process import ArmaProcess
np.random.seed(123)
ar = [1, .35, .8]
ma = [1, .8]
arma = ArmaProcess(ar, ma, nobs=100)
assert arma.isstationary()
assert arma.isinvertible()
y = arma.generate_sample()
dates = pd.date_range("1/1/1990", periods=len(y), freq='M')
ts = pd.Series(y, index=dates)
xpath = "/home/skipper/src/x12arima/x12a"
try:
results = x13_arima_analysis(xpath, ts)
except:
print("Caught exception")
results = x13_arima_analysis(xpath, ts, log=False)
# import pandas as pd
# <markdowncell> # * Let's make sure this models is estimable. # <codecell> arma_t = ArmaProcess(arparams, maparams) # <codecell> arma_t.isinvertible() # <codecell> arma_t.isstationary() # <rawcell> # * What does this mean? # <codecell> fig = plt.figure(figsize=(12,8)) ax = fig.add_subplot(111) ax.plot(arma_t.generate_sample(size=50)); # <codecell> arparams = np.array([1, .35, -.15, .55, .1]) maparams = np.array([1, .65])
axes[2].set_ylabel('Trend')
self.irregular.plot(ax=axes[3], legend=False)
axes[3].set_ylabel('Irregular')
fig.tight_layout()
return fig
if __name__ == "__main__":
import numpy as np
from statsmodels.tsa.arima_process import ArmaProcess
np.random.seed(123)
ar = [1, .35, .8]
ma = [1, .8]
arma = ArmaProcess(ar, ma, nobs=100)
assert arma.isstationary()
assert arma.isinvertible()
y = arma.generate_sample()
dates = pd.date_range("1/1/1990", periods=len(y), freq='M')
ts = pd.TimeSeries(y, index=dates)
xpath = "/home/skipper/src/x12arima/x12a"
try:
results = x13_arima_analysis(xpath, ts)
except:
print("Caught exception")
results = x13_arima_analysis(xpath, ts, log=False)
# import pandas as pd