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 # seas_y = pd.read_csv("usmelec.csv")
np.random.seed(1234) # include zero-th lag arparams = np.array([1, .75, -.65, -.55, .9]) maparams = np.array([1, .65]) # <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));
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 # seas_y = pd.read_csv("usmelec.csv")