def forecast(self, steps=1, exog=None, alpha=.05):
"""
Out-of-sample forecasts
Parameters
----------
steps : int
The number of out of sample forecasts from the end of the
sample.
exog : array
If the model is an ARMAX, you must provide out of sample
values for the exogenous variables. This should not include
the constant.
alpha : float
The confidence intervals for the forecasts are (1 - alpha) %
Returns
-------
forecast : array
Array of out of sample forecasts
stderr : array
Array of the standard error of the forecasts.
conf_int : array
2d array of the confidence interval for the forecast
"""
arparams = self.arparams
maparams = self.maparams
forecast = self.model._predict_out_of_sample(self.params,
steps, self.resid, exog)
# compute the standard errors
sigma2 = self.sigma2
ma_rep = arma2ma(np.r_[1,-arparams],
np.r_[1, maparams], nobs=steps)
fcasterr = np.sqrt(sigma2 * np.cumsum(ma_rep**2))
const = norm.ppf(1 - alpha/2.)
conf_int = np.c_[forecast - const*fcasterr, forecast + const*fcasterr]
return forecast, fcasterr, conf_int
def forecast(self, steps=1, exog=None, alpha=.05):
"""
Out-of-sample forecasts
Parameters
----------
steps : int
The number of out of sample forecasts from the end of the
sample.
exog : array
If the model is an ARMAX, you must provide out of sample
values for the exogenous variables. This should not include
the constant.
alpha : float
The confidence intervals for the forecasts are (1 - alpha) %
Returns
-------
forecast : array
Array of out of sample forecasts
stderr : array
Array of the standard error of the forecasts.
conf_int : array
2d array of the confidence interval for the forecast
"""
arparams = self.arparams
maparams = self.maparams
forecast = self.model._predict_out_of_sample(self.params, steps,
self.resid, exog)
# compute the standard errors
sigma2 = self.sigma2
ma_rep = arma2ma(np.r_[1, -arparams], np.r_[1, maparams], nobs=steps)
fcasterr = np.sqrt(sigma2 * np.cumsum(ma_rep**2))
const = norm.ppf(1 - alpha / 2.)
conf_int = np.c_[forecast - const * fcasterr,
forecast + const * fcasterr]
return forecast, fcasterr, conf_int
def forecast(self, steps=1, exog=None, alpha=.05):
"""
Out-of-sample forecasts
Parameters
----------
steps : int
The number of out of sample forecasts from the end of the
sample.
exog : array
If the model is an ARMAX, you must provide out of sample
values for the exogenous variables. This should not include
the constant.
alpha : float
The confidence intervals for the forecasts are (1 - alpha) %
Returns
-------
forecast : array
Array of out of sample forecasts
stderr : array
Array of the standard error of the forecasts.
conf_int : array
2d array of the confidence interval for the forecast
"""
k_trend, k_exog = self.k_trend, self.k_exog
if exog is None and k_exog > 0:
raise ValueError("You must provide exog for ARMAX")
q, p = self.k_ma, self.k_ar
maparams, arparams = self.maparams[::-1], self.arparams[::-1]
if q:
resid = np.zeros(2*q)
resid[:q] = self.resid[-q:] #only need last q
else:
i = -1 # since we don't run first loop below
y = self.model.endog
if k_trend == 1:
mu = self.params[0] * (1-arparams.sum()) # use expectation
# not constant
mu = np.array([mu]*steps) # repeat it so you can slice if exog
else:
mu = np.zeros(steps)
if k_exog > 0:
mu += np.dot(self.params[k_trend:k_exog+k_trend], exog)
endog = np.zeros(p+steps-1)
if p:
endog[:p] = y[-p:] #only need p
forecast = np.zeros(steps)
for i in range(q):
fcast = mu[i] + np.dot(arparams,endog[i:i+p]) + \
np.dot(maparams,resid[i:i+q])
forecast[i] = fcast
endog[i+p] = fcast
for i in range(i+1,steps-1):
fcast = mu[i] + np.dot(arparams,endog[i:i+p])
forecast[i] = fcast
endog[i+p] = fcast
#need to do one more without updating endog
forecast[-1] = mu[-1] + np.dot(arparams,endog[steps-1:])
#take care of exogenous
# compute the standard errors
sigma2 = self.sigma2
ma_rep = arma2ma(np.r_[1,-arparams[::-1]],
np.r_[1, maparams[::-1]], nobs=steps)
fcasterr = np.sqrt(sigma2 * np.cumsum(ma_rep**2))
const = norm.ppf(1 - alpha/2.)
conf_int = np.c_[forecast - const*fcasterr, forecast + const*fcasterr]
return forecast, fcasterr, conf_int
