def setup_class(cls):
# Test simple exponential smoothing (FPP: 7.1) against fpp::ses, with
# a fixed coefficient 0.6 and simple initialization
mod = ExponentialSmoothing(oildata, initialization_method='simple')
res = mod.filter([results_params['oil_fpp2']['alpha']])
super().setup_class('oil_fpp2', res)
def setup_class(cls):
oildata_copy = oildata.copy()
oildata_copy.name = ("oil", "data")
mod = ExponentialSmoothing(oildata_copy,
initialization_method='simple')
res = mod.filter([results_params['oil_fpp2']['alpha']])
super().setup_class('oil_fpp2', res)
def setup_class(cls):
# Test simple exponential smoothing (FPP: 7.1) against forecast::ets,
# with estimated coefficients
mod = ExponentialSmoothing(oildata,
initialization_method='estimated',
concentrate_scale=False)
res = mod.filter([
results_params['oil_ets']['alpha'],
results_params['oil_ets']['sigma2'],
results_params['oil_ets']['l0']
])
super().setup_class('oil_ets', res)
def setup_class(cls):
# Test Holt-Winters seasonal method (FPP: 7.5) with no trend
# against forecast::ets, with estimated coefficients
mod = ExponentialSmoothing(aust, seasonal=4, concentrate_scale=False)
params = np.r_[results_params['aust_ets3']['alpha'],
results_params['aust_ets3']['gamma'],
results_params['aust_ets3']['sigma2'],
results_params['aust_ets3']['l0'],
results_params['aust_ets3']['s0_0'],
results_params['aust_ets3']['s0_1'],
results_params['aust_ets3']['s0_2']]
res = mod.filter(params)
super().setup_class('aust_ets3', res)
def test_concentrated_initialization():
# Compare a model where initialization is concentrated out versus
# numarical maximum likelihood estimation
mod1 = ExponentialSmoothing(oildata, initialization_method='concentrated')
mod2 = ExponentialSmoothing(oildata)
# First, fix the other parameters at a particular value
res1 = mod1.filter([0.1])
res2 = mod2.fit_constrained({'smoothing_level': 0.1}, disp=0)
# Alternatively, estimate the remaining parameters
res1 = mod1.fit(disp=0)
res2 = mod2.fit(disp=0)
assert_allclose(res1.llf, res2.llf)
assert_allclose(res1.initial_state, res2.initial_state, rtol=1e-5)
def setup_class(cls):
# Test Holt's linear trend method (FPP: 7.2) with a damped trend
# against forecast::ets, with estimated coefficients
mod = ExponentialSmoothing(air,
trend=True,
damped_trend=True,
concentrate_scale=False)
params = [
results_params['air_ets']['alpha'],
results_params['air_ets']['beta'],
results_params['air_ets']['phi'],
results_params['air_ets']['sigma2'],
results_params['air_ets']['l0'], results_params['air_ets']['b0']
]
res = mod.filter(params)
super().setup_class('air_ets', res)
def setup_class(cls):
# Test Holt's linear trend method (FPP: 7.2) against fpp::holt,
# with fixed coefficients and simple initialization
mod = ExponentialSmoothing(air,
trend=True,
concentrate_scale=False,
initialization_method='simple')
# alpha, beta^*
params = [
results_params['air_fpp1']['alpha'],
results_params['air_fpp1']['beta_star'],
results_params['air_fpp1']['sigma2']
]
# beta = alpha * beta^*
params[1] = params[0] * params[1]
res = mod.filter(params)
super().setup_class('air_fpp1', res)
def setup_class(cls):
# Test Holt-Winters seasonal method (FPP: 7.5) against fpp::hw,
# with estimated coefficients
mod = ExponentialSmoothing(aust,
trend=True,
seasonal=4,
concentrate_scale=False)
params = np.r_[results_params['aust_fpp1']['alpha'],
results_params['aust_fpp1']['beta'],
results_params['aust_fpp1']['gamma'],
results_params['aust_fpp1']['sigma2'],
results_params['aust_fpp1']['l0'],
results_params['aust_fpp1']['b0'],
results_params['aust_fpp1']['s0_0'],
results_params['aust_fpp1']['s0_1'],
results_params['aust_fpp1']['s0_2']]
res = mod.filter(params)
super().setup_class('aust_fpp1', res)
