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ENH: Add complete ability to set parameters and start params
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Add ability to set start params
Add ability to set start values
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@bashtage
bashtage committed Aug 5, 2018
1 parent 47d5435 commit 6e89b5d
Showing 1 changed file with 62 additions and 36 deletions.
98 changes: 62 additions & 36 deletions statsmodels/tsa/holtwinters.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,6 @@
Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and practice. OTexts, 2014.
Author: Terence L van Zyl
"""
import numpy as np
import pandas as pd
Expand Down Expand Up @@ -401,7 +400,13 @@ def __init__(self, endog, trend=None, damped=False, seasonal=None,
self.seasonal_periods = seasonal_periods
if seasonal_periods is None:
try:
freq = pd.infer_freq(endog.index)
# TODO: Is a better version of this implemented somewhere?
freq = None
index = endog.index
if hasattr(index, 'freq') and hasattr(index.freq, 'freqstr'):
freq = endog.index.freq.freqstr
if not freq:
freq = pd.infer_freq(index)
self.seasonal_periods = _freq_to_period[freq]
except (ValueError, TypeError, AttributeError):
raise ValueError('Unable to detect seasonal periods.')
Expand Down Expand Up @@ -444,7 +449,8 @@ def predict(self, params, start=None, end=None):

def fit(self, smoothing_level=None, smoothing_slope=None, smoothing_seasonal=None,
damping_slope=None, optimized=True, use_boxcox=False, remove_bias=False,
use_basinhopping=False, start_params=None, initial_level=None, initial_slope=None):
use_basinhopping=False, start_params=None, initial_level=None, initial_slope=None,
use_brute=True):
"""
fit Holt Winter's Exponential Smoothing
Expand Down Expand Up @@ -482,6 +488,9 @@ def fit(self, smoothing_level=None, smoothing_slope=None, smoothing_seasonal=Non
Value to use when initializing the fitted level.
initial_slope: float, optional
Value to use when initializing the fitted slope.
use_brute: bool, optional
Search for good starting values using a brute force (grid) optimizer.
If False, a naive set of starting values is used.
Returns
-------
Expand Down Expand Up @@ -583,43 +592,60 @@ def fit(self, smoothing_level=None, smoothing_slope=None, smoothing_seasonal=Non
initial_level is None, False, False] + [False] * m)
func = func_dict[(None, None)]
p[:] = [init_alpha, init_beta, init_gamma, l0, b0, init_phi] + s0

# txi [alpha, beta, gamma, l0, b0, phi, s0,..,s_(m-1)]
# Have a quick look in the region for a good starting place for alpha etc.
# using guesstimates for the levels
txi = xi & np.array(
[True, True, True, False, False, True] + [False] * m)
txi = txi.astype(np.bool)
bounds = np.array([(0.0, 1.0), (0.0, 1.0), (0.0, 1.0),
(0.0, None), (0.0, None), (0.0, 1.0)] + [(None, None), ] * m)
args = (txi.astype(np.uint8), p, y, l, b, s, m, self.nobs, max_seen)
if start_params is None:
res = brute(func, bounds[txi], args, Ns=20, full_output=True, finish=None)
(p[txi], max_seen, grid, Jout) = res
else:
if np.any(xi):
# txi [alpha, beta, gamma, l0, b0, phi, s0,..,s_(m-1)]
# Have a quick look in the region for a good starting place for alpha etc.
# using guesstimates for the levels
txi = xi & np.array(
[True, True, True, False, False, True] + [False] * m)
txi = txi.astype(np.bool)
bounds = np.array([(0.0, 1.0), (0.0, 1.0), (0.0, 1.0),
(0.0, None), (0.0, None), (0.0, 1.0)] + [(None, None), ] * m)
args = (txi.astype(np.uint8), p, y, l, b, s, m, self.nobs, max_seen)
if start_params is None and np.any(txi) and use_brute:
res = brute(func, bounds[txi], args, Ns=20, full_output=True, finish=None)
(p[txi], max_seen, grid, Jout) = res
else:
if start_params is not None:
p[xi] = start_params
args = (xi.astype(np.uint8), p, y, l, b, s, m, self.nobs, max_seen)
max_seen = func(np.ascontiguousarray(p[xi]), *args)
# alpha, beta, gamma, l0, b0, phi = p[:6]
# s0 = p[6:]
# bounds = np.array([(0.0,1.0),(0.0,1.0),(0.0,1.0),(0.0,None),
# (0.0,None),(0.8,1.0)] + [(None,None),]*m)
args = (xi.astype(np.uint8), p, y, l, b, s, m, self.nobs, max_seen)
p[xi] = start_params
max_seen = func(np.ascontiguousarray(p[xi]), *args)
# alpha, beta, gamma, l0, b0, phi = p[:6]
# s0 = p[6:]
# bounds = np.array([(0.0,1.0),(0.0,1.0),(0.0,1.0),(0.0,None),
# (0.0,None),(0.8,1.0)] + [(None,None),]*m)
args = (xi.astype(np.uint8), p, y, l, b, s, m, self.nobs, max_seen)
if use_basinhopping:
# Take a deeper look in the local minimum we are in to find the best
# solution to parameters, maybe hop around to try escape the local
# minimum we may be in.
res = basinhopping(func, p[xi],
minimizer_kwargs={'args': args, 'bounds': bounds[xi]},
stepsize=0.01)
if use_basinhopping:
# Take a deeper look in the local minimum we are in to find the best
# solution to parameters, maybe hop around to try escape the local
# minimum we may be in.
res = basinhopping(func, p[xi],
minimizer_kwargs={'args': args, 'bounds': bounds[xi]},
stepsize=0.01)
success = res.lowest_optimization_result.success
else:
# Take a deeper look in the local minimum we are in to find the best
# solution to parameters
res = minimize(func, p[xi], args=args, bounds=bounds[xi])
success = res.success

if not success:
from warnings import warn
from statsmodels.tools.sm_exceptions import ConvergenceWarning
warn("Optimization failed to converge. Check mle_retvals.",
ConvergenceWarning)
p[xi] = res.x
opt = res
else:
# Take a deeper look in the local minimum we are in to find the best
# solution to parameters
res = minimize(func, p[xi], args=args, bounds=bounds[xi])
p[xi] = res.x
from warnings import warn
from statsmodels.tools.sm_exceptions import EstimationWarning
message = "Model has no free parameters to estimate. Set " \
"optimized=False to suppress this warning"
warn(message, EstimationWarning)

[alpha, beta, gamma, l0, b0, phi] = p[:6]
s0 = p[6:]
opt = res

hwfit = self._predict(h=0, smoothing_level=alpha, smoothing_slope=beta,
smoothing_seasonal=gamma, damping_slope=phi,
initial_level=l0, initial_slope=b0, initial_seasons=s0,
Expand Down

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