本文整理汇总了Python中statsmodels.tsa.statespace.kalman_filter.KalmanFilter.simulate方法的典型用法代码示例。如果您正苦于以下问题：Python KalmanFilter.simulate方法的具体用法？Python KalmanFilter.simulate怎么用？Python KalmanFilter.simulate使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类statsmodels.tsa.statespace.kalman_filter.KalmanFilter的用法示例。
在下文中一共展示了KalmanFilter.simulate方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_simulate
# 需要导入模块: from statsmodels.tsa.statespace.kalman_filter import KalmanFilter [as 别名]
# 或者: from statsmodels.tsa.statespace.kalman_filter.KalmanFilter import simulate [as 别名]
def test_simulate():
# Test for simulation of new time-series
from scipy.signal import lfilter
# Common parameters
nsimulations = 10
sigma2 = 2
measurement_shocks = np.zeros(nsimulations)
state_shocks = np.random.normal(scale=sigma2**0.5, size=nsimulations)
# Random walk model, so simulated series is just the cumulative sum of
# the shocks
mod = KalmanFilter(k_endog=1, k_states=1)
mod['design', 0, 0] = 1.
mod['transition', 0, 0] = 1.
mod['selection', 0, 0] = 1.
actual = mod.simulate(
nsimulations, measurement_shocks=measurement_shocks,
state_shocks=state_shocks)[0].squeeze()
desired = np.r_[0, np.cumsum(state_shocks)[:-1]]
assert_allclose(actual, desired)
# Local level model, so simulated series is just the cumulative sum of
# the shocks plus the measurement shock
mod = KalmanFilter(k_endog=1, k_states=1)
mod['design', 0, 0] = 1.
mod['transition', 0, 0] = 1.
mod['selection', 0, 0] = 1.
actual = mod.simulate(
nsimulations, measurement_shocks=np.ones(nsimulations),
state_shocks=state_shocks)[0].squeeze()
desired = np.r_[1, np.cumsum(state_shocks)[:-1] + 1]
assert_allclose(actual, desired)
# Local level-like model with observation and state intercepts, so
# simulated series is just the cumulative sum of the shocks minus the state
# intercept, plus the observation intercept and the measurement shock
mod = KalmanFilter(k_endog=1, k_states=1)
mod['obs_intercept', 0, 0] = 5.
mod['design', 0, 0] = 1.
mod['state_intercept', 0, 0] = -2.
mod['transition', 0, 0] = 1.
mod['selection', 0, 0] = 1.
actual = mod.simulate(
nsimulations, measurement_shocks=np.ones(nsimulations),
state_shocks=state_shocks)[0].squeeze()
desired = np.r_[1 + 5, np.cumsum(state_shocks - 2)[:-1] + 1 + 5]
assert_allclose(actual, desired)
# Model with time-varying observation intercept
mod = KalmanFilter(k_endog=1, k_states=1, nobs=10)
mod['obs_intercept'] = (np.arange(10)*1.).reshape(1, 10)
mod['design', 0, 0] = 1.
mod['transition', 0, 0] = 1.
mod['selection', 0, 0] = 1.
actual = mod.simulate(
nsimulations, measurement_shocks=measurement_shocks,
state_shocks=state_shocks)[0].squeeze()
desired = np.r_[0, np.cumsum(state_shocks)[:-1] + np.arange(1, 10)]
assert_allclose(actual, desired)
# Model with time-varying observation intercept, check that error is raised
# if more simulations are requested than are nobs.
mod = KalmanFilter(k_endog=1, k_states=1, nobs=10)
mod['obs_intercept'] = (np.arange(10)*1.).reshape(1, 10)
mod['design', 0, 0] = 1.
mod['transition', 0, 0] = 1.
mod['selection', 0, 0] = 1.
assert_raises(ValueError, mod.simulate, nsimulations+1, measurement_shocks,
state_shocks)
# ARMA(1,1): phi = [0.1], theta = [0.5], sigma^2 = 2
phi = 0.1
theta = 0.5
mod = sarimax.SARIMAX([0], order=(1, 0, 1))
mod.update(np.r_[phi, theta, sigma2])
actual = mod.ssm.simulate(
nsimulations, measurement_shocks=measurement_shocks,
state_shocks=state_shocks,
initial_state=np.zeros(mod.k_states))[0].squeeze()
desired = lfilter([1, theta], [1, -phi], np.r_[0, state_shocks[:-1]])
assert_allclose(actual, desired)
# SARIMAX(1,0,1)x(1,0,1,4), this time using the results object call
mod = sarimax.SARIMAX([0.1, 0.5, -0.2], order=(1, 0, 1),
seasonal_order=(1, 0, 1, 4))
res = mod.filter([0.1, 0.5, 0.2, -0.3, 1])
actual = res.simulate(
nsimulations, measurement_shocks=measurement_shocks,
#.........这里部分代码省略.........