def simple_exponential_smoothing_ci(stochastic_process):
N, t, alpha = 200, 160, 0.5
if stochastic_process == "GP":
x0 = 20
realisations = pd.Series(sample_gaussian_process(20, 5, N), range(N))
elif stochastic_process == "RW":
x0 = 0
realisations = pd.Series(list(sample_random_walk(0, N)), range(N))
else:
quit()
mod = ExponentialSmoothing(realisations[:t + 1],
initialization_method='known',
initial_level=x0).fit(disp=False)
print(mod.summary())
forecasts = mod.get_forecast(N - (t + 1))
forecasts_ci = forecasts.conf_int(alpha=0.05)
plot_ci(realisations,
pd.Series(np.nan, range(t + 1)).append(forecasts.predicted_mean),
forecasts_ci, alpha)
if stochastic_process == "GP":
plt.savefig('/Users/gwren/Downloads/36_ses_prediction_intervals.svg',
format='svg')
elif stochastic_process == "RW":
plt.savefig('/Users/gwren/Downloads/37_ses_gaussian_random_walk.svg',
format='svg')
else:
quit()
py.show()
def holt_ci():
N, t = 200, 160
realisations = pd.Series(list(sample_random_walk(0, 0.1, N)), range(N))
mod = ExponentialSmoothing(
realisations[:t + 1], trend=True,
initialization_method='estimated').fit(disp=False)
print(mod.summary())
forecasts = mod.get_forecast(N - (t + 1))
forecasts_ci = forecasts.conf_int(alpha=0.05)
plot_ci(realisations,
pd.Series(np.nan, range(t + 1)).append(forecasts.predicted_mean),
forecasts_ci)
py.show()
