Source code for statsmodels.tsa.statespace.structural

"""
Univariate structural time series models

Author: Chad Fulton
License: Simplified-BSD
"""
from __future__ import division, absolute_import, print_function

from warnings import warn
from collections import OrderedDict

import numpy as np

from statsmodels.tools.tools import Bunch
from statsmodels.tools.sm_exceptions import (ValueWarning, OutputWarning,
                                             SpecificationWarning)
import statsmodels.base.wrapper as wrap

from statsmodels.tsa.filters.hp_filter import hpfilter
from statsmodels.tsa.tsatools import lagmat

from .mlemodel import MLEModel, MLEResults, MLEResultsWrapper
from .initialization import Initialization
from .tools import (
    companion_matrix, constrain_stationary_univariate,
    unconstrain_stationary_univariate,
    prepare_exog
)

_mask_map = {
    1: 'irregular',
    2: 'fixed intercept',
    3: 'deterministic constant',
    6: 'random walk',
    7: 'local level',
    8: 'fixed slope',
    11: 'deterministic trend',
    14: 'random walk with drift',
    15: 'local linear deterministic trend',
    31: 'local linear trend',
    27: 'smooth trend',
    26: 'random trend'
}


[docs]class UnobservedComponents(MLEModel): r""" Univariate unobserved components time series model These are also known as structural time series models, and decompose a (univariate) time series into trend, seasonal, cyclical, and irregular components. Parameters ---------- level : bool or string, optional Whether or not to include a level component. Default is False. Can also be a string specification of the level / trend component; see Notes for available model specification strings. trend : bool, optional Whether or not to include a trend component. Default is False. If True, `level` must also be True. seasonal : int or None, optional The period of the seasonal component, if any. Default is None. freq_seasonal: list of dicts or None, optional. Whether (and how) to model seasonal component(s) with trig. functions. If specified, there is one dictionary for each frequency-domain seasonal component. Each dictionary must have the key, value pair for 'period' -- integer and may have a key, value pair for 'harmonics' -- integer. If 'harmonics' is not specified in any of the dictionaries, it defaults to the floor of period/2. cycle : bool, optional Whether or not to include a cycle component. Default is False. ar : int or None, optional The order of the autoregressive component. Default is None. exog : array_like or None, optional Exogenous variables. irregular : bool, optional Whether or not to include an irregular component. Default is False. stochastic_level : bool, optional Whether or not any level component is stochastic. Default is False. stochastic_trend : bool, optional Whether or not any trend component is stochastic. Default is False. stochastic_seasonal : bool, optional Whether or not any seasonal component is stochastic. Default is True. stochastic_freq_seasonal: list of bools, optional Whether or not each seasonal component(s) is (are) stochastic. Default is True for each component. The list should be of the same length as freq_seasonal. stochastic_cycle : bool, optional Whether or not any cycle component is stochastic. Default is False. damped_cycle : bool, optional Whether or not the cycle component is damped. Default is False. cycle_period_bounds : tuple, optional A tuple with lower and upper allowed bounds for the period of the cycle. If not provided, the following default bounds are used: (1) if no date / time information is provided, the frequency is constrained to be between zero and :math:`\pi`, so the period is constrained to be in [0.5, infinity]. (2) If the date / time information is provided, the default bounds allow the cyclical component to be between 1.5 and 12 years; depending on the frequency of the endogenous variable, this will imply different specific bounds. Notes ----- These models take the general form (see [1]_ Chapter 3.2 for all details) .. math:: y_t = \mu_t + \gamma_t + c_t + \varepsilon_t where :math:`y_t` refers to the observation vector at time :math:`t`, :math:`\mu_t` refers to the trend component, :math:`\gamma_t` refers to the seasonal component, :math:`c_t` refers to the cycle, and :math:`\varepsilon_t` is the irregular. The modeling details of these components are given below. **Trend** The trend component is a dynamic extension of a regression model that includes an intercept and linear time-trend. It can be written: .. math:: \mu_t = \mu_{t-1} + \beta_{t-1} + \eta_{t-1} \\ \beta_t = \beta_{t-1} + \zeta_{t-1} where the level is a generalization of the intercept term that can dynamically vary across time, and the trend is a generalization of the time-trend such that the slope can dynamically vary across time. Here :math:`\eta_t \sim N(0, \sigma_\eta^2)` and :math:`\zeta_t \sim N(0, \sigma_\zeta^2)`. For both elements (level and trend), we can consider models in which: - The element is included vs excluded (if the trend is included, there must also be a level included). - The element is deterministic vs stochastic (i.e. whether or not the variance on the error term is confined to be zero or not) The only additional parameters to be estimated via MLE are the variances of any included stochastic components. The level/trend components can be specified using the boolean keyword arguments `level`, `stochastic_level`, `trend`, etc., or all at once as a string argument to `level`. The following table shows the available model specifications: +----------------------------------+--------------------------------------+--------------------+--------------------------------------------------+ | Model name | Full string syntax | Abbreviated syntax | Model | +==================================+======================================+====================+==================================================+ | No trend | `'irregular'` | `'ntrend'` | .. math:: y_t &= \varepsilon_t | +----------------------------------+--------------------------------------+--------------------+--------------------------------------------------+ | Fixed intercept | `'fixed intercept'` | | .. math:: y_t &= \mu | +----------------------------------+--------------------------------------+--------------------+--------------------------------------------------+ | Deterministic constant | `'deterministic constant'` | `'dconstant'` | .. math:: y_t &= \mu + \varepsilon_t | +----------------------------------+--------------------------------------+--------------------+--------------------------------------------------+ | Local level | `'local level'` | `'llevel'` | .. math:: y_t &= \mu_t + \varepsilon_t \\ | | | | | \mu_t &= \mu_{t-1} + \eta_t | +----------------------------------+--------------------------------------+--------------------+--------------------------------------------------+ | Random walk | `'random walk'` | `'rwalk'` | .. math:: y_t &= \mu_t \\ | | | | | \mu_t &= \mu_{t-1} + \eta_t | +----------------------------------+--------------------------------------+--------------------+--------------------------------------------------+ | Fixed slope | `'fixed slope'` | | .. math:: y_t &= \mu_t \\ | | | | | \mu_t &= \mu_{t-1} + \beta | +----------------------------------+--------------------------------------+--------------------+--------------------------------------------------+ | Deterministic trend | `'deterministic trend'` | `'dtrend'` | .. math:: y_t &= \mu_t + \varepsilon_t \\ | | | | | \mu_t &= \mu_{t-1} + \beta | +----------------------------------+--------------------------------------+--------------------+--------------------------------------------------+ | Local linear deterministic trend | `'local linear deterministic trend'` | `'lldtrend'` | .. math:: y_t &= \mu_t + \varepsilon_t \\ | | | | | \mu_t &= \mu_{t-1} + \beta + \eta_t | +----------------------------------+--------------------------------------+--------------------+--------------------------------------------------+ | Random walk with drift | `'random walk with drift'` | `'rwdrift'` | .. math:: y_t &= \mu_t \\ | | | | | \mu_t &= \mu_{t-1} + \beta + \eta_t | +----------------------------------+--------------------------------------+--------------------+--------------------------------------------------+ | Local linear trend | `'local linear trend'` | `'lltrend'` | .. math:: y_t &= \mu_t + \varepsilon_t \\ | | | | | \mu_t &= \mu_{t-1} + \beta_{t-1} + \eta_t \\ | | | | | \beta_t &= \beta_{t-1} + \zeta_t | +----------------------------------+--------------------------------------+--------------------+--------------------------------------------------+ | Smooth trend | `'smooth trend'` | `'strend'` | .. math:: y_t &= \mu_t + \varepsilon_t \\ | | | | | \mu_t &= \mu_{t-1} + \beta_{t-1} \\ | | | | | \beta_t &= \beta_{t-1} + \zeta_t | +----------------------------------+--------------------------------------+--------------------+--------------------------------------------------+ | Random trend | `'random trend'` | `'rtrend'` | .. math:: y_t &= \mu_t \\ | | | | | \mu_t &= \mu_{t-1} + \beta_{t-1} \\ | | | | | \beta_t &= \beta_{t-1} + \zeta_t | +----------------------------------+--------------------------------------+--------------------+--------------------------------------------------+ Following the fitting of the model, the unobserved level and trend component time series are available in the results class in the `level` and `trend` attributes, respectively. **Seasonal (Time-domain)** The seasonal component is modeled as: .. math:: \gamma_t = - \sum_{j=1}^{s-1} \gamma_{t+1-j} + \omega_t \\ \omega_t \sim N(0, \sigma_\omega^2) The periodicity (number of seasons) is s, and the defining character is that (without the error term), the seasonal components sum to zero across one complete cycle. The inclusion of an error term allows the seasonal effects to vary over time (if this is not desired, :math:`\sigma_\omega^2` can be set to zero using the `stochastic_seasonal=False` keyword argument). This component results in one parameter to be selected via maximum likelihood: :math:`\sigma_\omega^2`, and one parameter to be chosen, the number of seasons `s`. Following the fitting of the model, the unobserved seasonal component time series is available in the results class in the `seasonal` attribute. ** Frequency-domain Seasonal** Each frequency-domain seasonal component is modeled as: .. math:: \gamma_t & = \sum_{j=1}^h \gamma_{j, t} \\ \gamma_{j, t+1} & = \gamma_{j, t}\cos(\lambda_j) + \gamma^{*}_{j, t}\sin(\lambda_j) + \omega_{j,t} \\ \gamma^{*}_{j, t+1} & = -\gamma^{(1)}_{j, t}\sin(\lambda_j) + \gamma^{*}_{j, t}\cos(\lambda_j) + \omega^{*}_{j, t}, \\ \omega^{*}_{j, t}, \omega_{j, t} & \sim N(0, \sigma_{\omega^2}) \\ \lambda_j & = \frac{2 \pi j}{s} where j ranges from 1 to h. The periodicity (number of "seasons" in a "year") is s and the number of harmonics is h. Note that h is configurable to be less than s/2, but s/2 harmonics is sufficient to fully model all seasonal variations of periodicity s. Like the time domain seasonal term (cf. Seasonal section, above), the inclusion of the error terms allows for the seasonal effects to vary over time. The argument stochastic_freq_seasonal can be used to set one or more of the seasonal components of this type to be non-random, meaning they will not vary over time. This component results in one parameter to be fitted using maximum likelihood: :math:`\sigma_{\omega^2}`, and up to two parameters to be chosen, the number of seasons s and optionally the number of harmonics h, with :math:`1 \leq h \leq \floor(s/2)`. After fitting the model, each unobserved seasonal component modeled in the frequency domain is available in the results class in the `freq_seasonal` attribute. **Cycle** The cyclical component is intended to capture cyclical effects at time frames much longer than captured by the seasonal component. For example, in economics the cyclical term is often intended to capture the business cycle, and is then expected to have a period between "1.5 and 12 years" (see Durbin and Koopman). .. math:: c_{t+1} & = \rho_c (\tilde c_t \cos \lambda_c t + \tilde c_t^* \sin \lambda_c) + \tilde \omega_t \\ c_{t+1}^* & = \rho_c (- \tilde c_t \sin \lambda_c t + \tilde c_t^* \cos \lambda_c) + \tilde \omega_t^* \\ where :math:`\omega_t, \tilde \omega_t iid N(0, \sigma_{\tilde \omega}^2)` The parameter :math:`\lambda_c` (the frequency of the cycle) is an additional parameter to be estimated by MLE. If the cyclical effect is stochastic (`stochastic_cycle=True`), then there is another parameter to estimate (the variance of the error term - note that both of the error terms here share the same variance, but are assumed to have independent draws). If the cycle is damped (`damped_cycle=True`), then there is a third parameter to estimate, :math:`\rho_c`. In order to achieve cycles with the appropriate frequencies, bounds are imposed on the parameter :math:`\lambda_c` in estimation. These can be controlled via the keyword argument `cycle_period_bounds`, which, if specified, must be a tuple of bounds on the **period** `(lower, upper)`. The bounds on the frequency are then calculated from those bounds. The default bounds, if none are provided, are selected in the following way: 1. If no date / time information is provided, the frequency is constrained to be between zero and :math:`\pi`, so the period is constrained to be in :math:`[0.5, \infty]`. 2. If the date / time information is provided, the default bounds allow the cyclical component to be between 1.5 and 12 years; depending on the frequency of the endogenous variable, this will imply different specific bounds. Following the fitting of the model, the unobserved cyclical component time series is available in the results class in the `cycle` attribute. **Irregular** The irregular components are independent and identically distributed (iid): .. math:: \varepsilon_t \sim N(0, \sigma_\varepsilon^2) **Autoregressive Irregular** An autoregressive component (often used as a replacement for the white noise irregular term) can be specified as: .. math:: \varepsilon_t = \rho(L) \varepsilon_{t-1} + \epsilon_t \\ \epsilon_t \sim N(0, \sigma_\epsilon^2) In this case, the AR order is specified via the `autoregressive` keyword, and the autoregressive coefficients are estimated. Following the fitting of the model, the unobserved autoregressive component time series is available in the results class in the `autoregressive` attribute. **Regression effects** Exogenous regressors can be pass to the `exog` argument. The regression coefficients will be estimated by maximum likelihood unless `mle_regression=False`, in which case the regression coefficients will be included in the state vector where they are essentially estimated via recursive OLS. If the regression_coefficients are included in the state vector, the recursive estimates are available in the results class in the `regression_coefficients` attribute. References ---------- .. [1] Durbin, James, and Siem Jan Koopman. 2012. Time Series Analysis by State Space Methods: Second Edition. Oxford University Press. """ # noqa:E501 def __init__(self, endog, level=False, trend=False, seasonal=None, freq_seasonal=None, cycle=False, autoregressive=None, exog=None, irregular=False, stochastic_level=False, stochastic_trend=False, stochastic_seasonal=True, stochastic_freq_seasonal=None, stochastic_cycle=False, damped_cycle=False, cycle_period_bounds=None, mle_regression=True, **kwargs): # Model options self.level = level self.trend = trend self.seasonal_periods = seasonal if seasonal is not None else 0 self.seasonal = self.seasonal_periods > 0 if freq_seasonal: self.freq_seasonal_periods = [d['period'] for d in freq_seasonal] self.freq_seasonal_harmonics = [d.get( 'harmonics', int(np.floor(d['period'] / 2))) for d in freq_seasonal] else: self.freq_seasonal_periods = [] self.freq_seasonal_harmonics = [] self.freq_seasonal = any(x > 0 for x in self.freq_seasonal_periods) self.cycle = cycle self.ar_order = autoregressive if autoregressive is not None else 0 self.autoregressive = self.ar_order > 0 self.irregular = irregular self.stochastic_level = stochastic_level self.stochastic_trend = stochastic_trend self.stochastic_seasonal = stochastic_seasonal if stochastic_freq_seasonal is None: self.stochastic_freq_seasonal = [True] * len( self.freq_seasonal_periods) else: if len(stochastic_freq_seasonal) != len(freq_seasonal): raise ValueError( "Length of stochastic_freq_seasonal must equal length" " of freq_seasonal: {!r} vs {!r}".format( len(stochastic_freq_seasonal), len(freq_seasonal))) self.stochastic_freq_seasonal = stochastic_freq_seasonal self.stochastic_cycle = stochastic_cycle self.damped_cycle = damped_cycle self.mle_regression = mle_regression # Check for string trend/level specification self.trend_specification = None if isinstance(self.level, str): self.trend_specification = level self.level = False # Check if any of the trend/level components have been set, and # reset everything to False trend_attributes = ['irregular', 'level', 'trend', 'stochastic_level', 'stochastic_trend'] for attribute in trend_attributes: if not getattr(self, attribute) is False: warn("Value of `%s` may be overridden when the trend" " component is specified using a model string." % attribute, SpecificationWarning) setattr(self, attribute, False) # Now set the correct specification spec = self.trend_specification if spec == 'irregular' or spec == 'ntrend': self.irregular = True self.trend_specification = 'irregular' elif spec == 'fixed intercept': self.level = True elif spec == 'deterministic constant' or spec == 'dconstant': self.irregular = True self.level = True self.trend_specification = 'deterministic constant' elif spec == 'local level' or spec == 'llevel': self.irregular = True self.level = True self.stochastic_level = True self.trend_specification = 'local level' elif spec == 'random walk' or spec == 'rwalk': self.level = True self.stochastic_level = True self.trend_specification = 'random walk' elif spec == 'fixed slope': self.level = True self.trend = True elif spec == 'deterministic trend' or spec == 'dtrend': self.irregular = True self.level = True self.trend = True self.trend_specification = 'deterministic trend' elif (spec == 'local linear deterministic trend' or spec == 'lldtrend'): self.irregular = True self.level = True self.stochastic_level = True self.trend = True self.trend_specification = 'local linear deterministic trend' elif spec == 'random walk with drift' or spec == 'rwdrift': self.level = True self.stochastic_level = True self.trend = True self.trend_specification = 'random walk with drift' elif spec == 'local linear trend' or spec == 'lltrend': self.irregular = True self.level = True self.stochastic_level = True self.trend = True self.stochastic_trend = True self.trend_specification = 'local linear trend' elif spec == 'smooth trend' or spec == 'strend': self.irregular = True self.level = True self.trend = True self.stochastic_trend = True self.trend_specification = 'smooth trend' elif spec == 'random trend' or spec == 'rtrend': self.level = True self.trend = True self.stochastic_trend = True self.trend_specification = 'random trend' else: raise ValueError("Invalid level/trend specification: '%s'" % spec) # Check for a model that makes sense if trend and not level: warn("Trend component specified without level component;" " deterministic level component added.", SpecificationWarning) self.level = True self.stochastic_level = False if not (self.irregular or (self.level and self.stochastic_level) or (self.trend and self.stochastic_trend) or (self.seasonal and self.stochastic_seasonal) or (self.freq_seasonal and any( self.stochastic_freq_seasonal)) or (self.cycle and self.stochastic_cycle) or self.autoregressive): warn("Specified model does not contain a stochastic element;" " irregular component added.", SpecificationWarning) self.irregular = True if self.seasonal and self.seasonal_periods < 2: raise ValueError('Seasonal component must have a seasonal period' ' of at least 2.') if self.freq_seasonal: for p in self.freq_seasonal_periods: if p < 2: raise ValueError( 'Frequency Domain seasonal component must have a ' 'seasonal period of at least 2.') # Create a bitmask holding the level/trend specification self.trend_mask = ( self.irregular * 0x01 | self.level * 0x02 | self.level * self.stochastic_level * 0x04 | self.trend * 0x08 | self.trend * self.stochastic_trend * 0x10 ) # Create the trend specification, if it wasn't given if self.trend_specification is None: # trend specification may be none, e.g. if the model is only # a stochastic cycle, etc. self.trend_specification = _mask_map.get(self.trend_mask, None) # Exogenous component (self.k_exog, exog) = prepare_exog(exog) self.regression = self.k_exog > 0 # Model parameters self._k_seasonal_states = (self.seasonal_periods - 1) * self.seasonal self._k_freq_seas_states = ( sum(2 * h for h in self.freq_seasonal_harmonics) * self.freq_seasonal) self._k_cycle_states = self.cycle * 2 k_states = ( self.level + self.trend + self._k_seasonal_states + self._k_freq_seas_states + self._k_cycle_states + self.ar_order + (not self.mle_regression) * self.k_exog ) k_posdef = ( self.stochastic_level * self.level + self.stochastic_trend * self.trend + self.stochastic_seasonal * self.seasonal + ((sum(2 * h if self.stochastic_freq_seasonal[ix] else 0 for ix, h in enumerate(self.freq_seasonal_harmonics))) * self.freq_seasonal) + self.stochastic_cycle * (self._k_cycle_states) + self.autoregressive ) # The ar states are initialized as stationary, so they don't need to be # burned. loglikelihood_burn = kwargs.get('loglikelihood_burn', k_states - self.ar_order) # We can still estimate the model with just the irregular component, # just need to have one state that does nothing. if k_states == 0: if not self.irregular: raise ValueError('Model has no components specified.') k_states = 1 if k_posdef == 0: k_posdef = 1 # Setup the representation super(UnobservedComponents, self).__init__( endog, k_states, k_posdef=k_posdef, exog=exog, **kwargs ) self.setup() # Set as time-varying model if we have exog if self.k_exog > 0: self.ssm._time_invariant = False # Initialize the model self.ssm.loglikelihood_burn = loglikelihood_burn # Need to reset the MLE names (since when they were first set, `setup` # had not been run (and could not have been at that point)) self.data.param_names = self.param_names # Get bounds for the frequency of the cycle, if we know the frequency # of the data. if cycle_period_bounds is None: freq = self.data.freq[0] if self.data.freq is not None else '' if freq == 'A': cycle_period_bounds = (1.5, 12) elif freq == 'Q': cycle_period_bounds = (1.5*4, 12*4) elif freq == 'M': cycle_period_bounds = (1.5*12, 12*12) else: # If we have no information on data frequency, require the # cycle frequency to be between 0 and pi cycle_period_bounds = (2, np.inf) self.cycle_frequency_bound = ( 2*np.pi / cycle_period_bounds[1], 2*np.pi / cycle_period_bounds[0] ) # update _init_keys attached by super self._init_keys += ['level', 'trend', 'seasonal', 'freq_seasonal', 'cycle', 'autoregressive', 'exog', 'irregular', 'stochastic_level', 'stochastic_trend', 'stochastic_seasonal', 'stochastic_freq_seasonal', 'stochastic_cycle', 'damped_cycle', 'cycle_period_bounds', 'mle_regression'] + list(kwargs.keys()) # TODO: I think the kwargs or not attached, need to recover from ??? # Initialize the state self.initialize_default() def _get_init_kwds(self): # Get keywords based on model attributes kwds = super(UnobservedComponents, self)._get_init_kwds() # Modifications kwds['seasonal'] = self.seasonal_periods kwds['freq_seasonal'] = [ {'period': p, 'harmonics': self.freq_seasonal_harmonics[ix]} for ix, p in enumerate(self.freq_seasonal_periods)] kwds['autoregressive'] = self.ar_order for key, value in kwds.items(): if value is None and hasattr(self.ssm, key): kwds[key] = getattr(self.ssm, key) return kwds
[docs] def setup(self): """ Setup the structural time series representation """ # Initialize the ordered sets of parameters self.parameters = OrderedDict() self.parameters_obs_intercept = OrderedDict() self.parameters_obs_cov = OrderedDict() self.parameters_transition = OrderedDict() self.parameters_state_cov = OrderedDict() # Initialize the fixed components of the state space matrices, i = 0 # state offset j = 0 # state covariance offset if self.irregular: self.parameters_obs_cov['irregular_var'] = 1 if self.level: self.ssm['design', 0, i] = 1. self.ssm['transition', i, i] = 1. if self.trend: self.ssm['transition', i, i+1] = 1. if self.stochastic_level: self.ssm['selection', i, j] = 1. self.parameters_state_cov['level_var'] = 1 j += 1 i += 1 if self.trend: self.ssm['transition', i, i] = 1. if self.stochastic_trend: self.ssm['selection', i, j] = 1. self.parameters_state_cov['trend_var'] = 1 j += 1 i += 1 if self.seasonal: n = self.seasonal_periods - 1 self.ssm['design', 0, i] = 1. self.ssm['transition', i:i + n, i:i + n] = ( companion_matrix(np.r_[1, [1] * n]).transpose() ) if self.stochastic_seasonal: self.ssm['selection', i, j] = 1. self.parameters_state_cov['seasonal_var'] = 1 j += 1 i += n if self.freq_seasonal: for ix, h in enumerate(self.freq_seasonal_harmonics): # These are the \gamma_jt and \gamma^*_jt terms in D&K (3.8) n = 2 * h p = self.freq_seasonal_periods[ix] lambda_p = 2 * np.pi / float(p) t = 0 # frequency transition matrix offset for block in range(1, h + 1): # ibid. eqn (3.7) self.ssm['design', 0, i+t] = 1. # ibid. eqn (3.8) cos_lambda_block = np.cos(lambda_p * block) sin_lambda_block = np.sin(lambda_p * block) trans = np.array([[cos_lambda_block, sin_lambda_block], [-sin_lambda_block, cos_lambda_block]]) trans_s = np.s_[i + t:i + t + 2] self.ssm['transition', trans_s, trans_s] = trans t += 2 if self.stochastic_freq_seasonal[ix]: self.ssm['selection', i:i + n, j:j + n] = np.eye(n) cov_key = 'freq_seasonal_var_{!r}'.format(ix) self.parameters_state_cov[cov_key] = 1 j += n i += n if self.cycle: self.ssm['design', 0, i] = 1. self.parameters_transition['cycle_freq'] = 1 if self.damped_cycle: self.parameters_transition['cycle_damp'] = 1 if self.stochastic_cycle: self.ssm['selection', i:i+2, j:j+2] = np.eye(2) self.parameters_state_cov['cycle_var'] = 1 j += 2 self._idx_cycle_transition = np.s_['transition', i:i+2, i:i+2] i += 2 if self.autoregressive: self.ssm['design', 0, i] = 1. self.parameters_transition['ar_coeff'] = self.ar_order self.parameters_state_cov['ar_var'] = 1 self.ssm['selection', i, j] = 1 self.ssm['transition', i:i+self.ar_order, i:i+self.ar_order] = ( companion_matrix(self.ar_order).T ) self._idx_ar_transition = ( np.s_['transition', i, i:i+self.ar_order] ) j += 1 i += self.ar_order if self.regression: if self.mle_regression: self.parameters_obs_intercept['reg_coeff'] = self.k_exog else: design = np.repeat(self.ssm['design', :, :, 0], self.nobs, axis=0) self.ssm['design'] = design.transpose()[np.newaxis, :, :] self.ssm['design', 0, i:i+self.k_exog, :] = ( self.exog.transpose()) self.ssm['transition', i:i+self.k_exog, i:i+self.k_exog] = ( np.eye(self.k_exog) ) i += self.k_exog # Update to get the actual parameter set self.parameters.update(self.parameters_obs_cov) self.parameters.update(self.parameters_state_cov) self.parameters.update(self.parameters_transition) # ordered last self.parameters.update(self.parameters_obs_intercept) self.k_obs_intercept = sum(self.parameters_obs_intercept.values()) self.k_obs_cov = sum(self.parameters_obs_cov.values()) self.k_transition = sum(self.parameters_transition.values()) self.k_state_cov = sum(self.parameters_state_cov.values()) self.k_params = sum(self.parameters.values()) # Other indices idx = np.diag_indices(self.ssm.k_posdef) self._idx_state_cov = ('state_cov', idx[0], idx[1]) # Some of the variances may be tied together (repeated parameter usage) # Use list() for compatibility with python 3.5 param_keys = list(self.parameters_state_cov.keys()) self._var_repetitions = np.ones(self.k_state_cov, dtype=np.int) if self.freq_seasonal: for ix, is_stochastic in enumerate(self.stochastic_freq_seasonal): if is_stochastic: num_harmonics = self.freq_seasonal_harmonics[ix] repeat_times = 2 * num_harmonics cov_key = 'freq_seasonal_var_{!r}'.format(ix) cov_ix = param_keys.index(cov_key) self._var_repetitions[cov_ix] = repeat_times if self.stochastic_cycle and self.cycle: cov_ix = param_keys.index('cycle_var') self._var_repetitions[cov_ix] = 2 self._repeat_any_var = any(self._var_repetitions > 1)
[docs] def initialize_default(self, approximate_diffuse_variance=None): if approximate_diffuse_variance is None: approximate_diffuse_variance = self.ssm.initial_variance init = Initialization( self.k_states, approximate_diffuse_variance=approximate_diffuse_variance) if self.autoregressive: offset = (self.level + self.trend + self._k_seasonal_states + self._k_freq_seas_states + self._k_cycle_states) length = self.ar_order init.set((0, offset), 'approximate_diffuse') init.set((offset, offset + length), 'stationary') init.set((offset + length, self.k_states), 'approximate_diffuse') # If we do not have an autoregressive component, then everything has # a diffuse initialization else: init.set(None, 'approximate_diffuse') self.ssm.initialization = init
@property def _res_classes(self): return {'fit': (UnobservedComponentsResults, UnobservedComponentsResultsWrapper)} @property def start_params(self): if not hasattr(self, 'parameters'): return [] # Eliminate missing data to estimate starting parameters endog = self.endog exog = self.exog if np.any(np.isnan(endog)): mask = ~np.isnan(endog).squeeze() endog = endog[mask] if exog is not None: exog = exog[mask] # Level / trend variances # (Use the HP filter to get initial estimates of variances) _start_params = {} if self.level: resid, trend1 = hpfilter(endog) if self.stochastic_trend: cycle2, trend2 = hpfilter(trend1) _start_params['trend_var'] = np.std(trend2)**2 if self.stochastic_level: _start_params['level_var'] = np.std(cycle2)**2 elif self.stochastic_level: _start_params['level_var'] = np.std(trend1)**2 else: resid = self.ssm.endog[0] # Regression if self.regression and self.mle_regression: _start_params['reg_coeff'] = ( np.linalg.pinv(exog).dot(resid).tolist() ) resid = np.squeeze( resid - np.dot(exog, _start_params['reg_coeff']) ) # Autoregressive if self.autoregressive: Y = resid[self.ar_order:] X = lagmat(resid, self.ar_order, trim='both') _start_params['ar_coeff'] = np.linalg.pinv(X).dot(Y).tolist() resid = np.squeeze(Y - np.dot(X, _start_params['ar_coeff'])) _start_params['ar_var'] = np.var(resid) # The variance of the residual term can be used for all variances, # just to get something in the right order of magnitude. var_resid = np.var(resid) # Seasonal if self.stochastic_seasonal: _start_params['seasonal_var'] = var_resid # Frequency domain seasonal for ix, is_stochastic in enumerate(self.stochastic_freq_seasonal): cov_key = 'freq_seasonal_var_{!r}'.format(ix) _start_params[cov_key] = var_resid # Cyclical if self.cycle: _start_params['cycle_var'] = var_resid # Clip this to make sure it is postive and strictly stationary # (i.e. don't want negative or 1) _start_params['cycle_damp'] = np.clip( np.linalg.pinv(resid[:-1, None]).dot(resid[1:])[0], 0, 0.99 ) # Set initial period estimate to 3 year, if we know the frequency # of the data observations freq = self.data.freq[0] if self.data.freq is not None else '' if freq == 'A': _start_params['cycle_freq'] = 2 * np.pi / 3 elif freq == 'Q': _start_params['cycle_freq'] = 2 * np.pi / 12 elif freq == 'M': _start_params['cycle_freq'] = 2 * np.pi / 36 else: if not np.any(np.isinf(self.cycle_frequency_bound)): _start_params['cycle_freq'] = ( np.mean(self.cycle_frequency_bound)) elif np.isinf(self.cycle_frequency_bound[1]): _start_params['cycle_freq'] = self.cycle_frequency_bound[0] else: _start_params['cycle_freq'] = self.cycle_frequency_bound[1] # Irregular if self.irregular: _start_params['irregular_var'] = var_resid # Create the starting parameter list start_params = [] for key in self.parameters.keys(): if np.isscalar(_start_params[key]): start_params.append(_start_params[key]) else: start_params += _start_params[key] return start_params @property def param_names(self): if not hasattr(self, 'parameters'): return [] param_names = [] for key in self.parameters.keys(): if key == 'irregular_var': param_names.append('sigma2.irregular') elif key == 'level_var': param_names.append('sigma2.level') elif key == 'trend_var': param_names.append('sigma2.trend') elif key == 'seasonal_var': param_names.append('sigma2.seasonal') elif key.startswith('freq_seasonal_var_'): # There are potentially multiple frequency domain # seasonal terms idx_fseas_comp = int(key[-1]) periodicity = self.freq_seasonal_periods[idx_fseas_comp] harmonics = self.freq_seasonal_harmonics[idx_fseas_comp] freq_seasonal_name = "{p}({h})".format( p=repr(periodicity), h=repr(harmonics)) param_names.append( 'sigma2.' + 'freq_seasonal_' + freq_seasonal_name) elif key == 'cycle_var': param_names.append('sigma2.cycle') elif key == 'cycle_freq': param_names.append('frequency.cycle') elif key == 'cycle_damp': param_names.append('damping.cycle') elif key == 'ar_coeff': for i in range(self.ar_order): param_names.append('ar.L%d' % (i+1)) elif key == 'ar_var': param_names.append('sigma2.ar') elif key == 'reg_coeff': param_names += [ 'beta.%s' % self.exog_names[i] for i in range(self.k_exog) ] else: param_names.append(key) return param_names
[docs] def transform_params(self, unconstrained): """ Transform unconstrained parameters used by the optimizer to constrained parameters used in likelihood evaluation """ unconstrained = np.array(unconstrained, ndmin=1) constrained = np.zeros(unconstrained.shape, dtype=unconstrained.dtype) # Positive parameters: obs_cov, state_cov offset = self.k_obs_cov + self.k_state_cov constrained[:offset] = unconstrained[:offset]**2 # Cycle parameters if self.cycle: # Cycle frequency must be between between our bounds low, high = self.cycle_frequency_bound constrained[offset] = ( 1 / (1 + np.exp(-unconstrained[offset])) ) * (high - low) + low offset += 1 # Cycle damping (if present) must be between 0 and 1 if self.damped_cycle: constrained[offset] = ( 1 / (1 + np.exp(-unconstrained[offset])) ) offset += 1 # Autoregressive coefficients must be stationary if self.autoregressive: constrained[offset:offset + self.ar_order] = ( constrain_stationary_univariate( unconstrained[offset:offset + self.ar_order] ) ) offset += self.ar_order # Nothing to do with betas constrained[offset:offset + self.k_exog] = ( unconstrained[offset:offset + self.k_exog] ) return constrained
[docs] def untransform_params(self, constrained): """ Reverse the transformation """ constrained = np.array(constrained, ndmin=1) unconstrained = np.zeros(constrained.shape, dtype=constrained.dtype) # Positive parameters: obs_cov, state_cov offset = self.k_obs_cov + self.k_state_cov unconstrained[:offset] = constrained[:offset]**0.5 # Cycle parameters if self.cycle: # Cycle frequency must be between between our bounds low, high = self.cycle_frequency_bound x = (constrained[offset] - low) / (high - low) unconstrained[offset] = np.log( x / (1 - x) ) offset += 1 # Cycle damping (if present) must be between 0 and 1 if self.damped_cycle: unconstrained[offset] = np.log( constrained[offset] / (1 - constrained[offset]) ) offset += 1 # Autoregressive coefficients must be stationary if self.autoregressive: unconstrained[offset:offset + self.ar_order] = ( unconstrain_stationary_univariate( constrained[offset:offset + self.ar_order] ) ) offset += self.ar_order # Nothing to do with betas unconstrained[offset:offset + self.k_exog] = ( constrained[offset:offset + self.k_exog] ) return unconstrained
[docs] def update(self, params, **kwargs): params = super(UnobservedComponents, self).update(params, **kwargs) offset = 0 # Observation covariance if self.irregular: self.ssm['obs_cov', 0, 0] = params[offset] offset += 1 # State covariance if self.k_state_cov > 0: variances = params[offset:offset+self.k_state_cov] if self._repeat_any_var: variances = np.repeat(variances, self._var_repetitions) self.ssm[self._idx_state_cov] = variances offset += self.k_state_cov # Cycle transition if self.cycle: cos_freq = np.cos(params[offset]) sin_freq = np.sin(params[offset]) cycle_transition = np.array( [[cos_freq, sin_freq], [-sin_freq, cos_freq]] ) if self.damped_cycle: offset += 1 cycle_transition *= params[offset] self.ssm[self._idx_cycle_transition] = cycle_transition offset += 1 # AR transition if self.autoregressive: self.ssm[self._idx_ar_transition] = ( params[offset:offset+self.ar_order] ) offset += self.ar_order # Beta observation intercept if self.regression: if self.mle_regression: self.ssm['obs_intercept'] = np.dot( self.exog, params[offset:offset+self.k_exog] )[None, :] offset += self.k_exog
[docs]class UnobservedComponentsResults(MLEResults): """ Class to hold results from fitting an unobserved components model. Parameters ---------- model : UnobservedComponents instance The fitted model instance Attributes ---------- specification : dictionary Dictionary including all attributes from the unobserved components model instance. See Also -------- statsmodels.tsa.statespace.kalman_filter.FilterResults statsmodels.tsa.statespace.mlemodel.MLEResults """ def __init__(self, model, params, filter_results, cov_type='opg', **kwargs): super(UnobservedComponentsResults, self).__init__( model, params, filter_results, cov_type, **kwargs) self.df_resid = np.inf # attribute required for wald tests # Save _init_kwds self._init_kwds = self.model._get_init_kwds() # Save number of states by type self._k_states_by_type = { 'seasonal': self.model._k_seasonal_states, 'freq_seasonal': self.model._k_freq_seas_states, 'cycle': self.model._k_cycle_states} # Save the model specification self.specification = Bunch(**{ # Model options 'level': self.model.level, 'trend': self.model.trend, 'seasonal_periods': self.model.seasonal_periods, 'seasonal': self.model.seasonal, 'freq_seasonal': self.model.freq_seasonal, 'freq_seasonal_periods': self.model.freq_seasonal_periods, 'freq_seasonal_harmonics': self.model.freq_seasonal_harmonics, 'cycle': self.model.cycle, 'ar_order': self.model.ar_order, 'autoregressive': self.model.autoregressive, 'irregular': self.model.irregular, 'stochastic_level': self.model.stochastic_level, 'stochastic_trend': self.model.stochastic_trend, 'stochastic_seasonal': self.model.stochastic_seasonal, 'stochastic_freq_seasonal': self.model.stochastic_freq_seasonal, 'stochastic_cycle': self.model.stochastic_cycle, 'damped_cycle': self.model.damped_cycle, 'regression': self.model.regression, 'mle_regression': self.model.mle_regression, 'k_exog': self.model.k_exog, # Check for string trend/level specification 'trend_specification': self.model.trend_specification }) @property def level(self): """ Estimates of unobserved level component Returns ------- out: Bunch Has the following attributes: - `filtered`: a time series array with the filtered estimate of the component - `filtered_cov`: a time series array with the filtered estimate of the variance/covariance of the component - `smoothed`: a time series array with the smoothed estimate of the component - `smoothed_cov`: a time series array with the smoothed estimate of the variance/covariance of the component - `offset`: an integer giving the offset in the state vector where this component begins """ # If present, level is always the first component of the state vector out = None spec = self.specification if spec.level: offset = 0 out = Bunch(filtered=self.filtered_state[offset], filtered_cov=self.filtered_state_cov[offset, offset], smoothed=None, smoothed_cov=None, offset=offset) if self.smoothed_state is not None: out.smoothed = self.smoothed_state[offset] if self.smoothed_state_cov is not None: out.smoothed_cov = self.smoothed_state_cov[offset, offset] return out @property def trend(self): """ Estimates of of unobserved trend component Returns ------- out: Bunch Has the following attributes: - `filtered`: a time series array with the filtered estimate of the component - `filtered_cov`: a time series array with the filtered estimate of the variance/covariance of the component - `smoothed`: a time series array with the smoothed estimate of the component - `smoothed_cov`: a time series array with the smoothed estimate of the variance/covariance of the component - `offset`: an integer giving the offset in the state vector where this component begins """ # If present, trend is always the second component of the state vector # (because level is always present if trend is present) out = None spec = self.specification if spec.trend: offset = int(spec.level) out = Bunch(filtered=self.filtered_state[offset], filtered_cov=self.filtered_state_cov[offset, offset], smoothed=None, smoothed_cov=None, offset=offset) if self.smoothed_state is not None: out.smoothed = self.smoothed_state[offset] if self.smoothed_state_cov is not None: out.smoothed_cov = self.smoothed_state_cov[offset, offset] return out @property def seasonal(self): """ Estimates of unobserved seasonal component Returns ------- out: Bunch Has the following attributes: - `filtered`: a time series array with the filtered estimate of the component - `filtered_cov`: a time series array with the filtered estimate of the variance/covariance of the component - `smoothed`: a time series array with the smoothed estimate of the component - `smoothed_cov`: a time series array with the smoothed estimate of the variance/covariance of the component - `offset`: an integer giving the offset in the state vector where this component begins """ # If present, seasonal always follows level/trend (if they are present) # Note that we return only the first seasonal state, but there are # in fact seasonal_periods-1 seasonal states, however latter states # are just lagged versions of the first seasonal state. out = None spec = self.specification if spec.seasonal: offset = int(spec.trend + spec.level) out = Bunch(filtered=self.filtered_state[offset], filtered_cov=self.filtered_state_cov[offset, offset], smoothed=None, smoothed_cov=None, offset=offset) if self.smoothed_state is not None: out.smoothed = self.smoothed_state[offset] if self.smoothed_state_cov is not None: out.smoothed_cov = self.smoothed_state_cov[offset, offset] return out @property def freq_seasonal(self): """ Estimates of unobserved frequency domain seasonal component(s) Returns ------- out: list of Bunch instances Each item has the following attributes: - `filtered`: a time series array with the filtered estimate of the component - `filtered_cov`: a time series array with the filtered estimate of the variance/covariance of the component - `smoothed`: a time series array with the smoothed estimate of the component - `smoothed_cov`: a time series array with the smoothed estimate of the variance/covariance of the component - `offset`: an integer giving the offset in the state vector where this component begins """ # If present, freq_seasonal components always follows level/trend # and seasonal. # There are 2 * (harmonics) seasonal states per freq_seasonal # component. # The sum of every other state enters the measurement equation. # Additionally, there can be multiple components of this type. # These facts make this property messier in implementation than the # others. # Fortunately, the states are conditionally mutually independent # (conditional on previous timestep's states), so that the calculations # of the variances are simple summations of individual variances and # the calculation of the returned state is likewise a summation. out = [] spec = self.specification if spec.freq_seasonal: previous_states_offset = int(spec.trend + spec.level + self._k_states_by_type['seasonal']) previous_f_seas_offset = 0 for ix, h in enumerate(spec.freq_seasonal_harmonics): offset = previous_states_offset + previous_f_seas_offset period = spec.freq_seasonal_periods[ix] # Only the gamma_jt terms enter the measurement equation (cf. # D&K 2012 (3.7)) states_in_sum = np.arange(0, 2 * h, 2) filtered_state = np.sum( [self.filtered_state[offset + j] for j in states_in_sum], axis=0) filtered_cov = np.sum( [self.filtered_state_cov[offset + j, offset + j] for j in states_in_sum], axis=0) item = Bunch( filtered=filtered_state, filtered_cov=filtered_cov, smoothed=None, smoothed_cov=None, offset=offset, pretty_name='seasonal {p}({h})'.format(p=repr(period), h=repr(h))) if self.smoothed_state is not None: item.smoothed = np.sum( [self.smoothed_state[offset+j] for j in states_in_sum], axis=0) if self.smoothed_state_cov is not None: item.smoothed_cov = np.sum( [self.smoothed_state_cov[offset+j, offset+j] for j in states_in_sum], axis=0) out.append(item) previous_f_seas_offset += 2 * h return out @property def cycle(self): """ Estimates of unobserved cycle component Returns ------- out: Bunch Has the following attributes: - `filtered`: a time series array with the filtered estimate of the component - `filtered_cov`: a time series array with the filtered estimate of the variance/covariance of the component - `smoothed`: a time series array with the smoothed estimate of the component - `smoothed_cov`: a time series array with the smoothed estimate of the variance/covariance of the component - `offset`: an integer giving the offset in the state vector where this component begins """ # If present, cycle always follows level/trend, seasonal, and freq # seasonal. # Note that we return only the first cyclical state, but there are # in fact 2 cyclical states. The second cyclical state is not simply # a lag of the first cyclical state, but the first cyclical state is # the one that enters the measurement equation. out = None spec = self.specification if spec.cycle: offset = int(spec.trend + spec.level + self._k_states_by_type['seasonal'] + self._k_states_by_type['freq_seasonal']) out = Bunch(filtered=self.filtered_state[offset], filtered_cov=self.filtered_state_cov[offset, offset], smoothed=None, smoothed_cov=None, offset=offset) if self.smoothed_state is not None: out.smoothed = self.smoothed_state[offset] if self.smoothed_state_cov is not None: out.smoothed_cov = self.smoothed_state_cov[offset, offset] return out @property def autoregressive(self): """ Estimates of unobserved autoregressive component Returns ------- out: Bunch Has the following attributes: - `filtered`: a time series array with the filtered estimate of the component - `filtered_cov`: a time series array with the filtered estimate of the variance/covariance of the component - `smoothed`: a time series array with the smoothed estimate of the component - `smoothed_cov`: a time series array with the smoothed estimate of the variance/covariance of the component - `offset`: an integer giving the offset in the state vector where this component begins """ # If present, autoregressive always follows level/trend, seasonal, # freq seasonal, and cyclical. # If it is an AR(p) model, then there are p associated # states, but the second - pth states are just lags of the first state. out = None spec = self.specification if spec.autoregressive: offset = int(spec.trend + spec.level + self._k_states_by_type['seasonal'] + self._k_states_by_type['freq_seasonal'] + self._k_states_by_type['cycle']) out = Bunch(filtered=self.filtered_state[offset], filtered_cov=self.filtered_state_cov[offset, offset], smoothed=None, smoothed_cov=None, offset=offset) if self.smoothed_state is not None: out.smoothed = self.smoothed_state[offset] if self.smoothed_state_cov is not None: out.smoothed_cov = self.smoothed_state_cov[offset, offset] return out @property def regression_coefficients(self): """ Estimates of unobserved regression coefficients Returns ------- out: Bunch Has the following attributes: - `filtered`: a time series array with the filtered estimate of the component - `filtered_cov`: a time series array with the filtered estimate of the variance/covariance of the component - `smoothed`: a time series array with the smoothed estimate of the component - `smoothed_cov`: a time series array with the smoothed estimate of the variance/covariance of the component - `offset`: an integer giving the offset in the state vector where this component begins """ # If present, state-vector regression coefficients always are last # (i.e. they follow level/trend, seasonal, freq seasonal, cyclical, and # autoregressive states). There is one state associated with each # regressor, and all are returned here. out = None spec = self.specification if spec.regression: if spec.mle_regression: import warnings warnings.warn('Regression coefficients estimated via maximum' ' likelihood. Estimated coefficients are' ' available in the parameters list, not as part' ' of the state vector.', OutputWarning) else: offset = int(spec.trend + spec.level + self._k_states_by_type['seasonal'] + self._k_states_by_type['freq_seasonal'] + self._k_states_by_type['cycle'] + spec.ar_order) start = offset end = offset + spec.k_exog out = Bunch( filtered=self.filtered_state[start:end], filtered_cov=self.filtered_state_cov[start:end, start:end], smoothed=None, smoothed_cov=None, offset=offset ) if self.smoothed_state is not None: out.smoothed = self.smoothed_state[start:end] if self.smoothed_state_cov is not None: out.smoothed_cov = ( self.smoothed_state_cov[start:end, start:end]) return out
[docs] def plot_components(self, which=None, alpha=0.05, observed=True, level=True, trend=True, seasonal=True, freq_seasonal=True, cycle=True, autoregressive=True, legend_loc='upper right', fig=None, figsize=None): """ Plot the estimated components of the model. Parameters ---------- which : {'filtered', 'smoothed'}, or None, optional Type of state estimate to plot. Default is 'smoothed' if smoothed results are available otherwise 'filtered'. alpha : float, optional The confidence intervals for the components are (1 - alpha) % level : boolean, optional Whether or not to plot the level component, if applicable. Default is True. trend : boolean, optional Whether or not to plot the trend component, if applicable. Default is True. seasonal : boolean, optional Whether or not to plot the seasonal component, if applicable. Default is True. freq_seasonal: boolean, optional Whether or not to plot the frequency domain seasonal component(s), if applicable. Default is True. cycle : boolean, optional Whether or not to plot the cyclical component, if applicable. Default is True. autoregressive : boolean, optional Whether or not to plot the autoregressive state, if applicable. Default is True. fig : Matplotlib Figure instance, optional If given, subplots are created in this figure instead of in a new figure. Note that the grid will be created in the provided figure using `fig.add_subplot()`. figsize : tuple, optional If a figure is created, this argument allows specifying a size. The tuple is (width, height). Notes ----- If all options are included in the model and selected, this produces a 6x1 plot grid with the following plots (ordered top-to-bottom): 0. Observed series against predicted series 1. Level 2. Trend 3. Seasonal 4. Freq Seasonal 5. Cycle 6. Autoregressive Specific subplots will be removed if the component is not present in the estimated model or if the corresponding keywork argument is set to False. All plots contain (1 - `alpha`) % confidence intervals. """ from scipy.stats import norm from statsmodels.graphics.utils import _import_mpl, create_mpl_fig plt = _import_mpl() fig = create_mpl_fig(fig, figsize) # Determine which results we have if which is None: which = 'filtered' if self.smoothed_state is None else 'smoothed' # Determine which plots we have spec = self.specification comp = [ ('level', level and spec.level), ('trend', trend and spec.trend), ('seasonal', seasonal and spec.seasonal), ] if freq_seasonal and spec.freq_seasonal: for ix, _ in enumerate(spec.freq_seasonal_periods): key = 'freq_seasonal_{!r}'.format(ix) comp.append((key, True)) comp.extend( [('cycle', cycle and spec.cycle), ('autoregressive', autoregressive and spec.autoregressive)]) components = OrderedDict(comp) llb = self.filter_results.loglikelihood_burn # Number of plots k_plots = observed + np.sum(list(components.values())) # Get dates, if applicable if hasattr(self.data, 'dates') and self.data.dates is not None: dates = self.data.dates._mpl_repr() else: dates = np.arange(len(self.data.endog)) # Get the critical value for confidence intervals critical_value = norm.ppf(1 - alpha / 2.) plot_idx = 1 # Observed, predicted, confidence intervals if observed: ax = fig.add_subplot(k_plots, 1, plot_idx) plot_idx += 1 # Plot the observed dataset ax.plot(dates[llb:], self.model.endog[llb:], color='k', label='Observed') # Get the predicted values and confidence intervals predict = self.filter_results.forecasts[0] std_errors = np.sqrt(self.filter_results.forecasts_error_cov[0, 0]) ci_lower = predict - critical_value * std_errors ci_upper = predict + critical_value * std_errors # Plot ax.plot(dates[llb:], predict[llb:], label='One-step-ahead predictions') ci_poly = ax.fill_between( dates[llb:], ci_lower[llb:], ci_upper[llb:], alpha=0.2 ) ci_label = '$%.3g \\%%$ confidence interval' % ((1 - alpha) * 100) # Proxy artist for fill_between legend entry # See e.g. http://matplotlib.org/1.3.1/users/legend_guide.html p = plt.Rectangle((0, 0), 1, 1, fc=ci_poly.get_facecolor()[0]) # Legend handles, labels = ax.get_legend_handles_labels() handles.append(p) labels.append(ci_label) ax.legend(handles, labels, loc=legend_loc) ax.set_title('Predicted vs observed') # Plot each component for component, is_plotted in components.items(): if not is_plotted: continue ax = fig.add_subplot(k_plots, 1, plot_idx) plot_idx += 1 try: component_bunch = getattr(self, component) title = component.title() except AttributeError: # This might be a freq_seasonal component, of which there are # possibly multiple bagged up in property freq_seasonal if component.startswith('freq_seasonal_'): ix = int(component.replace('freq_seasonal_', '')) big_bunch = getattr(self, 'freq_seasonal') component_bunch = big_bunch[ix] title = component_bunch.pretty_name else: raise # Check for a valid estimation type if which not in component_bunch: raise ValueError('Invalid type of state estimate.') which_cov = '%s_cov' % which # Get the predicted values value = component_bunch[which] # Plot state_label = '%s (%s)' % (title, which) ax.plot(dates[llb:], value[llb:], label=state_label) # Get confidence intervals if which_cov in component_bunch: std_errors = np.sqrt(component_bunch['%s_cov' % which]) ci_lower = value - critical_value * std_errors ci_upper = value + critical_value * std_errors ci_poly = ax.fill_between( dates[llb:], ci_lower[llb:], ci_upper[llb:], alpha=0.2 ) ci_label = ('$%.3g \\%%$ confidence interval' % ((1 - alpha) * 100)) # Legend ax.legend(loc=legend_loc) ax.set_title('%s component' % title) # Add a note if first observations excluded if llb > 0: text = ('Note: The first %d observations are not shown, due to' ' approximate diffuse initialization.') fig.text(0.1, 0.01, text % llb, fontsize='large') return fig
[docs] def get_prediction(self, start=None, end=None, dynamic=False, index=None, exog=None, **kwargs): """ In-sample prediction and out-of-sample forecasting Parameters ---------- start : int, str, or datetime, optional Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. Default is the the zeroth observation. end : int, str, or datetime, optional Zero-indexed observation number at which to end forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. However, if the dates index does not have a fixed frequency, end must be an integer index if you want out of sample prediction. Default is the last observation in the sample. exog : array_like, optional If the model includes exogenous regressors, you must provide exactly enough out-of-sample values for the exogenous variables if end is beyond the last observation in the sample. dynamic : boolean, int, str, or datetime, optional Integer offset relative to `start` at which to begin dynamic prediction. Can also be an absolute date string to parse or a datetime type (these are not interpreted as offsets). Prior to this observation, true endogenous values will be used for prediction; starting with this observation and continuing through the end of prediction, forecasted endogenous values will be used instead. full_results : boolean, optional If True, returns a FilterResults instance; if False returns a tuple with forecasts, the forecast errors, and the forecast error covariance matrices. Default is False. **kwargs Additional arguments may required for forecasting beyond the end of the sample. See `FilterResults.predict` for more details. Returns ------- forecast : array Array of out of sample forecasts. """ if start is None: start = self.model._index[0] # Handle end (e.g. date) _start, _end, _out_of_sample, prediction_index = ( self.model._get_prediction_index(start, end, index, silent=True)) # Handle exogenous parameters if _out_of_sample and self.model.k_exog > 0: # Create a new faux model for the extended dataset nobs = self.model.data.orig_endog.shape[0] + _out_of_sample endog = np.zeros((nobs, self.model.k_endog)) if self.model.k_exog > 0: if exog is None: raise ValueError('Out-of-sample forecasting in a model' ' with a regression component requires' ' additional exogenous values via the' ' `exog` argument.') exog = np.array(exog) required_exog_shape = (_out_of_sample, self.model.k_exog) if not exog.shape == required_exog_shape: raise ValueError('Provided exogenous values are not of the' ' appropriate shape. Required %s, got %s.' % (str(required_exog_shape), str(exog.shape))) exog = np.c_[self.model.data.orig_exog.T, exog.T].T model_kwargs = self._init_kwds.copy() model_kwargs['exog'] = exog model = UnobservedComponents(endog, **model_kwargs) model.update(self.params) # Set the kwargs with the update time-varying state space # representation matrices for name in self.filter_results.shapes.keys(): if name == 'obs': continue mat = getattr(model.ssm, name) if mat.shape[-1] > 1: if len(mat.shape) == 2: kwargs[name] = mat[:, -_out_of_sample:] else: kwargs[name] = mat[:, :, -_out_of_sample:] elif self.model.k_exog == 0 and exog is not None: # TODO: UserWarning warn('Exogenous array provided to predict, but additional data not' ' required. `exog` argument ignored.', ValueWarning) return super(UnobservedComponentsResults, self).get_prediction( start=start, end=end, dynamic=dynamic, index=index, exog=exog, **kwargs)
[docs] def summary(self, alpha=.05, start=None): # Create the model name model_name = [self.specification.trend_specification] if self.specification.seasonal: seasonal_name = ('seasonal(%d)' % self.specification.seasonal_periods) if self.specification.stochastic_seasonal: seasonal_name = 'stochastic ' + seasonal_name model_name.append(seasonal_name) if self.specification.freq_seasonal: for ix, is_stochastic in enumerate( self.specification.stochastic_freq_seasonal): periodicity = self.specification.freq_seasonal_periods[ix] harmonics = self.specification.freq_seasonal_harmonics[ix] freq_seasonal_name = "freq_seasonal({p}({h}))".format( p=repr(periodicity), h=repr(harmonics)) if is_stochastic: freq_seasonal_name = 'stochastic ' + freq_seasonal_name model_name.append(freq_seasonal_name) if self.specification.cycle: cycle_name = 'cycle' if self.specification.stochastic_cycle: cycle_name = 'stochastic ' + cycle_name if self.specification.damped_cycle: cycle_name = 'damped ' + cycle_name model_name.append(cycle_name) if self.specification.autoregressive: autoregressive_name = 'AR(%d)' % self.specification.ar_order model_name.append(autoregressive_name) return super(UnobservedComponentsResults, self).summary( alpha=alpha, start=start, title='Unobserved Components Results', model_name=model_name )
summary.__doc__ = MLEResults.summary.__doc__
class UnobservedComponentsResultsWrapper(MLEResultsWrapper): _attrs = {} _wrap_attrs = wrap.union_dicts(MLEResultsWrapper._wrap_attrs, _attrs) _methods = {} _wrap_methods = wrap.union_dicts(MLEResultsWrapper._wrap_methods, _methods) wrap.populate_wrapper(UnobservedComponentsResultsWrapper, # noqa:E305 UnobservedComponentsResults)