# Source code for pyramid.arima.auto

# -*- coding: utf-8 -*-
#
# Author: Taylor Smith <taylor.smith@alkaline-ml.com>
#
# Automatically find optimal parameters for an ARIMA

from __future__ import absolute_import

from sklearn.utils.validation import check_array, column_or_1d
from sklearn.utils import check_random_state
from sklearn.externals.joblib import Parallel, delayed
from sklearn.linear_model import LinearRegression

from numpy.linalg import LinAlgError
import numpy as np

import warnings
import time

from .utils import ndiffs, is_constant, nsdiffs
from ..utils import diff, is_iterable
from .arima import ARIMA
from .warnings import ModelFitWarning

# for python 3 compat
from ..compat.python import xrange
from ..compat.numpy import DTYPE

__all__ = [
'auto_arima'
]

# The valid information criteria
VALID_CRITERIA = {'aic', 'aicc', 'bic', 'hqic', 'oob'}

[docs]def auto_arima(y, exogenous=None, start_p=2, d=None, start_q=2, max_p=5, max_d=2, max_q=5, start_P=1, D=None, start_Q=1, max_P=2, max_D=1, max_Q=2, max_order=10, m=1, seasonal=True, stationary=False, information_criterion='aic', alpha=0.05, test='kpss', seasonal_test='ch', stepwise=True, n_jobs=1, start_params=None, trend='c', method=None, transparams=True, solver='lbfgs', maxiter=50, disp=0, callback=None, offset_test_args=None, seasonal_test_args=None, suppress_warnings=False, error_action='warn', trace=False, random=False, random_state=None, n_fits=10, return_valid_fits=False, out_of_sample_size=0, scoring='mse', scoring_args=None, **fit_args): """Automatically discover the optimal order for an ARIMA model. The auto_arima function seeks to identify the most optimal parameters for an ARIMA model, and returns a fitted ARIMA model. This function is based on the commonly-used R function, forecast::auto.arima [3]. The auro_arima function works by conducting differencing tests (i.e., Kwiatkowski–Phillips–Schmidt–Shin, Augmented Dickey-Fuller or Phillips–Perron) to determine the order of differencing, d, and then fitting models within ranges of defined start_p, max_p, start_q, max_q ranges. If the seasonal optional is enabled, auto_arima also seeks to identify the optimal P and Q hyper- parameters after conducting the Canova-Hansen to determine the optimal order of seasonal differencing, D. In order to find the best model, auto_arima optimizes for a given information_criterion, one of {'aic', 'aicc', 'bic', 'hqic', 'oob'} (Akaike Information Criterion, Corrected Akaike Information Criterion, Bayesian Information Criterion, Hannan-Quinn Information Criterion, or "out of bag"--for validation scoring--respectively) and returns the ARIMA which minimizes the value. Note that due to stationarity issues, auto_arima might not find a suitable model that will converge. If this is the case, a ValueError will be thrown suggesting stationarity-inducing measures be taken prior to re-fitting or that a new range of order values be selected. Non- stepwise (i.e., essentially a grid search) selection can be slow, especially for seasonal data. Stepwise algorithm is outlined in Hyndman and Khandakar (2008). Parameters ---------- y : array-like or iterable, shape=(n_samples,) The time-series to which to fit the ARIMA estimator. This may either be a Pandas Series object (statsmodels can internally use the dates in the index), or a numpy array. This should be a one-dimensional array of floats, and should not contain any np.nan or np.inf values. exogenous : array-like, shape=[n_obs, n_vars], optional (default=None) An optional 2-d array of exogenous variables. If provided, these variables are used as additional features in the regression operation. This should not include a constant or trend. Note that if an ARIMA is fit on exogenous features, it must be provided exogenous features for making predictions. start_p : int, optional (default=2) The starting value of p, the order (or number of time lags) of the auto-regressive ("AR") model. Must be a positive integer. d : int, optional (default=None) The order of first-differencing. If None (by default), the value will automatically be selected based on the results of the test (i.e., either the Kwiatkowski–Phillips–Schmidt–Shin, Augmented Dickey-Fuller or the Phillips–Perron test will be conducted to find the most probable value). Must be a positive integer or None. Note that if d is None, the runtime could be significantly longer. start_q : int, optional (default=2) The starting value of q, the order of the moving-average ("MA") model. Must be a positive integer. max_p : int, optional (default=5) The maximum value of p, inclusive. Must be a positive integer greater than or equal to start_p. max_d : int, optional (default=2) The maximum value of d, or the maximum number of non-seasonal differences. Must be a positive integer greater than or equal to d. max_q : int, optional (default=5) The maximum value of q, inclusive. Must be a positive integer greater than start_q. start_P : int, optional (default=1) The starting value of P, the order of the auto-regressive portion of the seasonal model. D : int, optional (default=None) The order of the seasonal differencing. If None (by default, the value will automatically be selected based on the results of the seasonal_test. Must be a positive integer or None. start_Q : int, optional (default=1) The starting value of Q, the order of the moving-average portion of the seasonal model. max_P : int, optional (default=2) The maximum value of P, inclusive. Must be a positive integer greater than start_P. max_D : int, optional (default=1) The maximum value of D. Must be a positive integer greater than D. max_Q : int, optional (default=2) The maximum value of Q, inclusive. Must be a positive integer greater than start_Q. max_order : int, optional (default=10) If the sum of p and q is >= max_order, a model will *not* be fit with those parameters, but will progress to the next combination. Default is 5. If max_order is None, it means there are no constraints on maximum order. m : int, optional (default=1) The period for seasonal differencing, m refers to the number of periods in each season. For example, m is 4 for quarterly data, 12 for monthly data, or 1 for annual (non-seasonal) data. Default is 1. Note that if m == 1 (i.e., is non-seasonal), seasonal will be set to False. For more information on setting this parameter, see :ref:period. seasonal : bool, optional (default=True) Whether to fit a seasonal ARIMA. Default is True. Note that if seasonal is True and m == 1, seasonal will be set to False. stationary : bool, optional (default=False) Whether the time-series is stationary and d should be set to zero. information_criterion : str, optional (default='aic') The information criterion used to select the best ARIMA model. One of pyramid.arima.auto_arima.VALID_CRITERIA, ('aic', 'bic', 'hqic', 'oob'). alpha : float, optional (default=0.05) Level of the test for testing significance. test : str, optional (default='kpss') Type of unit root test to use in order to detect stationarity if stationary is False and d is None. Default is 'kpss' (Kwiatkowski–Phillips–Schmidt–Shin). seasonal_test : str, optional (default='ch') This determines which seasonal unit root test is used if seasonal is True and D is None. Default is 'ch' (Canova-Hansen). stepwise : bool, optional (default=True) Whether to use the stepwise algorithm outlined in Hyndman and Khandakar (2008) to identify the optimal model parameters. The stepwise algorithm can be significantly faster than fitting all (or a random subset of) hyper-parameter combinations and is less likely to over-fit the model. n_jobs : int, optional (default=1) The number of models to fit in parallel in the case of a grid search (stepwise=False). Default is 1, but -1 can be used to designate "as many as possible". start_params : array-like, optional (default=None) Starting parameters for ARMA(p,q). If None, the default is given by ARMA._fit_start_params. transparams : bool, optional (default=True) Whether or not to transform the parameters to ensure stationarity. Uses the transformation suggested in Jones (1980). If False, no checking for stationarity or invertibility is done. method : str, one of {'css-mle','mle','css'}, optional (default=None) This is the loglikelihood to maximize. If "css-mle", the conditional sum of squares likelihood is maximized and its values are used as starting values for the computation of the exact likelihood via the Kalman filter. If "mle", the exact likelihood is maximized via the Kalman Filter. If "css" the conditional sum of squares likelihood is maximized. All three methods use start_params as starting parameters. See above for more information. If fitting a seasonal ARIMA, the default is 'lbfgs' trend : str or iterable, optional (default='c') Parameter controlling the deterministic trend polynomial :math:A(t). Can be specified as a string where 'c' indicates a constant (i.e. a degree zero component of the trend polynomial), 't' indicates a linear trend with time, and 'ct' is both. Can also be specified as an iterable defining the polynomial as in numpy.poly1d, where [1,1,0,1] would denote :math:a + bt + ct^3. solver : str or None, optional (default='lbfgs') Solver to be used. The default is 'lbfgs' (limited memory Broyden-Fletcher-Goldfarb-Shanno). Other choices are 'bfgs', 'newton' (Newton-Raphson), 'nm' (Nelder-Mead), 'cg' - (conjugate gradient), 'ncg' (non-conjugate gradient), and 'powell'. By default, the limited memory BFGS uses m=12 to approximate the Hessian, projected gradient tolerance of 1e-8 and factr = 1e2. You can change these by using kwargs. maxiter : int, optional (default=50) The maximum number of function evaluations. Default is 50. disp : int, optional (default=0) If True, convergence information is printed. For the default 'lbfgs' solver, disp controls the frequency of the output during the iterations. disp < 0 means no output in this case. callback : callable, optional (default=None) Called after each iteration as callback(xk) where xk is the current parameter vector. This is only used in non-seasonal ARIMA models. offset_test_args : dict, optional (default=None) The args to pass to the constructor of the offset (d) test. See pyramid.arima.stationarity for more details. seasonal_test_args : dict, optional (default=None) The args to pass to the constructor of the seasonal offset (D) test. See pyramid.arima.seasonality for more details. suppress_warnings : bool, optional (default=False) Many warnings might be thrown inside of statsmodels. If suppress_warnings is True, all of the warnings coming from ARIMA will be squelched. error_action : str, optional (default='warn') If unable to fit an ARIMA due to stationarity issues, whether to warn ('warn'), raise the ValueError ('raise') or ignore ('ignore'). Note that the default behavior is to warn, and fits that fail will be returned as None. This is the recommended behavior, as statsmodels ARIMA and SARIMAX models hit bugs periodically that can cause an otherwise healthy parameter combination to fail for reasons not related to pyramid. trace : bool, optional (default=False) Whether to print status on the fits. Note that this can be very verbose... random : bool, optional (default=False) Similar to grid searches, auto_arima provides the capability to perform a "random search" over a hyper-parameter space. If random is True, rather than perform an exhaustive search or stepwise search, only n_fits ARIMA models will be fit (stepwise must be False for this option to do anything). random_state : int, long or numpy RandomState, optional (default=None) The PRNG for when random=True. Ensures replicable testing and results. n_fits : int, optional (default=10) If random is True and a "random search" is going to be performed, n_iter is the number of ARIMA models to be fit. return_valid_fits : bool, optional (default=False) If True, will return all valid ARIMA fits in a list. If False (by default), will only return the best fit. out_of_sample_size : int, optional (default=0) The ARIMA class can fit only a portion of the data if specified, in order to retain an "out of bag" sample score. This is the number of examples from the tail of the time series to hold out and use as validation examples. The model will not be fit on these samples, but the observations will be added into the model's endog and exog arrays so that future forecast values originate from the end of the endogenous vector. For instance:: y = [0, 1, 2, 3, 4, 5, 6] out_of_sample_size = 2 > Fit on: [0, 1, 2, 3, 4] > Score on: [5, 6] > Append [5, 6] to end of self.arima_res_.data.endog values scoring : str, optional (default='mse') If performing validation (i.e., if out_of_sample_size > 0), the metric to use for scoring the out-of-sample data. One of {'mse', 'mae'} scoring_args : dict, optional (default=None) A dictionary of key-word arguments to be passed to the scoring metric. **fit_args : dict, optional (default=None) A dictionary of keyword arguments to pass to the :func:ARIMA.fit method. See Also -------- :func:pyramid.arima.ARIMA Notes ----- Fitting with stepwise=False can prove slower, especially when seasonal=True. References ---------- .. [1] https://wikipedia.org/wiki/Autoregressive_integrated_moving_average .. [2] R's auto-arima source code: http://bit.ly/2gOh5z2 .. [3] R's auto-arima documentation: http://bit.ly/2wbBvUN """ start = time.time() # validate start/max points if any(_ < 0 for _ in (max_p, max_q, max_P, max_Q, start_p, start_q, start_P, start_Q)): raise ValueError('starting and max p, q, P & Q values must ' 'be positive integers (>= 0)') if max_p < start_p or max_q < start_q \ or max_P < start_P or max_Q < start_Q: raise ValueError('max p, q, P & Q must be >= than ' 'their starting values') # validate max_order if max_order is None: max_order = np.inf elif max_order < 0: raise ValueError('max_order must be None or a positive integer (>= 0)') # alternatively, if the start_p and start_q supercede # the max order, that's a failable offense... if (not seasonal and start_p + start_q > max_order) or \ (seasonal and start_P + start_p + start_Q + start_q > max_order): raise ValueError('If max_order is prescribed, it must ' 'exceed sum of starting orders') # validate d & D for _d, _max_d in ((d, max_d), (D, max_D)): if _max_d < 0: raise ValueError('max_d & max_D must be positive integers (>= 0)') if _d is not None: if _d < 0: raise ValueError('d & D must be None or a positive ' 'integer (>= 0)') # v0.9.0+ - ignore this if it's explicitly set... # if _d > _max_d: # raise ValueError('if explicitly defined, d & D must be <= ' # 'max_d & <= max_D, respectively') # is stepwise AND parallel enabled? if stepwise and n_jobs != 1: n_jobs = 1 warnings.warn('stepwise model cannot be fit in parallel (n_jobs=%i). ' 'Falling back to stepwise parameter search.' % n_jobs) # check on m if m < 1: raise ValueError('m must be a positive integer (> 0)') # check on n_iter if random and n_fits < 0: raise ValueError('n_iter must be a positive integer ' 'for a random search') # validate error action actions = {'warn', 'raise', 'ignore', None} if error_action not in actions: raise ValueError('error_action must be one of %r, but got %r' % (actions, error_action)) # copy array y = column_or_1d(check_array(y, ensure_2d=False, dtype=DTYPE, copy=True, force_all_finite=True)) # type: np.ndarray n_samples = y.shape[0] # check for constant data if is_constant(y): warnings.warn('Input time-series is completely constant; ' 'returning a (0, 0, 0) ARMA.') return _return_wrapper(_post_ppc_arima( _fit_arima(y, xreg=exogenous, order=(0, 0, 0), seasonal_order=None, start_params=start_params, trend=trend, method=method, transparams=transparams, solver=solver, maxiter=maxiter, disp=disp, callback=callback, fit_params=fit_args, suppress_warnings=suppress_warnings, trace=trace, error_action=error_action, scoring=scoring, out_of_sample_size=out_of_sample_size, scoring_args=scoring_args)), return_valid_fits, start, trace) # test ic, and use AIC if n <= 3 if information_criterion not in VALID_CRITERIA: raise ValueError('auto_arima not defined for information_criteria=%s. ' 'Valid information criteria include: %r' % (information_criterion, VALID_CRITERIA)) # the R code handles this, but I don't think statsmodels # will even fit a model this small... # if n_samples <= 3: # if information_criterion != 'aic': # warnings.warn('n_samples (%i) <= 3 ' # 'necessitates using AIC' % n_samples) # information_criterion = 'aic' # adjust max p, q -- R code: # max.p <- min(max.p, floor(serieslength/3)) # max.q <- min(max.q, floor(serieslength/3)) max_p = int(min(max_p, np.floor(n_samples / 3))) max_q = int(min(max_q, np.floor(n_samples / 3))) # this is not in the R code and poses a risk that R did not consider... # if max_p|q has now dropped below start_p|q, correct it. start_p = min(start_p, max_p) start_q = min(start_q, max_q) # if it's not seasonal, we can avoid multiple 'if not is None' comparisons # later by just using this shortcut (hack): if not seasonal: D = m = -1 # choose the order of differencing xx = y.copy() if exogenous is not None: lm = LinearRegression().fit(exogenous, y) xx = y - lm.predict(exogenous) # is the TS stationary? if stationary: d = D = 0 # now for seasonality if m == 1: D = max_P = max_Q = 0 # m must be > 1 for nsdiffs elif D is None: # we don't have a D yet and we need one (seasonal) seasonal_test_args = seasonal_test_args if seasonal_test_args is \ not None else dict() D = nsdiffs(xx, m=m, test=seasonal_test, max_D=max_D, **seasonal_test_args) if D > 0 and exogenous is not None: diffxreg = diff(exogenous, differences=D, lag=m) # check for constance on any column if np.apply_along_axis(is_constant, arr=diffxreg, axis=0).any(): D -= 1 # D might still be None if not seasonal. Py 3 will throw and error for that # unless we explicitly check for seasonal if D > 0: dx = diff(xx, differences=D, lag=m) else: dx = xx # If D was too big, we might have gotten rid of x altogether! if dx.shape[0] == 0: raise ValueError("The seasonal differencing order, D=%i, was too " "large for your time series, and after differencing, " "there are no samples remaining in your data. " "Try a smaller value for D, or if you didn't set D " "to begin with, try setting it explicitly. This can " "also occur in seasonal settings when m is too large." % D) # difference the exogenous matrix if exogenous is not None: if D > 0: diffxreg = diff(exogenous, differences=D, lag=m) else: diffxreg = exogenous else: # here's the thing... we're only going to use diffxreg if exogenous # was not None in the first place. However, PyCharm doesn't know that # and it thinks we might use it before assigning it. Therefore, assign # it to None as a default value and it won't raise the warning anymore. diffxreg = None # determine/set the order of differencing by estimating the number of # orders it would take in order to make the TS stationary. if d is None: offset_test_args = offset_test_args if offset_test_args is \ not None else dict() d = ndiffs(dx, test=test, alpha=alpha, max_d=max_d, **offset_test_args) if d > 0 and exogenous is not None: diffxreg = diff(diffxreg, differences=d, lag=1) # if any columns are constant, subtract one order of differencing if np.apply_along_axis(is_constant, arr=diffxreg, axis=0).any(): d -= 1 # check differences (do we want to warn?...) if error_action == 'warn' and not suppress_warnings: if D >= 2: warnings.warn("Having more than one seasonal differences is " "not recommended. Please consider using only one " "seasonal difference.") # if D is -1, this will be off, so we include the OR elif D + d > 2 or d > 2: warnings.warn("Having 3 or more differencing operations is not " "recommended. Please consider reducing the total " "number of differences.") if d > 0: dx = diff(dx, differences=d, lag=1) # check for constance if is_constant(dx): if exogenous is None and not (D > 0 or d < 2): raise ValueError('data follow a simple polynomial and are not ' 'suitable for ARIMA modeling') # perfect regression ssn = None if not seasonal else (0, D, 0, m) return _return_wrapper( _post_ppc_arima(_fit_arima(y, xreg=exogenous, order=(0, d, 0), seasonal_order=ssn, start_params=start_params, trend=trend, method=method, transparams=transparams, solver=solver, maxiter=maxiter, disp=disp, callback=callback, fit_params=fit_args, suppress_warnings=suppress_warnings, trace=trace, error_action=error_action, scoring=scoring, out_of_sample_size=out_of_sample_size, scoring_args=scoring_args)), return_valid_fits, start, trace) # seasonality issues if m > 1: if max_P > 0: max_p = min(max_p, m - 1) if max_Q > 0: max_q = min(max_q, m - 1) # generate the set of (p, q, P, Q) FIRST, since it is contingent # on whether or not the user is interested in a seasonal ARIMA result. # This will reduce the search space for non-seasonal ARIMA models. # loop p, q. Make sure to loop at +1 interval, # since max_{p|q} is inclusive. if seasonal: gen = ( ((p, d, q), (P, D, Q, m)) for p in xrange(start_p, max_p + 1) for q in xrange(start_q, max_q + 1) for P in xrange(start_P, max_P + 1) for Q in xrange(start_Q, max_Q + 1) if p + q + P + Q <= max_order ) else: # otherwise it's not seasonal, and we don't need the seasonal pieces gen = ( ((p, d, q), None) for p in xrange(start_p, max_p + 1) for q in xrange(start_q, max_q + 1) if p + q <= max_order ) if not stepwise: # if we are fitting a random search rather than an exhaustive one, we # will scramble up the generator (as a list) and only fit n_iter ARIMAs if random: random_state = check_random_state(random_state) # make a list to scramble... gen = random_state.permutation(list(gen))[:n_fits] # get results in parallel all_res = Parallel(n_jobs=n_jobs)( delayed(_fit_arima)(y, xreg=exogenous, order=order, seasonal_order=seasonal_order, start_params=start_params, trend=trend, method=method, transparams=transparams, solver=solver, maxiter=maxiter, disp=disp, callback=callback, fit_params=fit_args, suppress_warnings=suppress_warnings, trace=trace, error_action=error_action, out_of_sample_size=out_of_sample_size, scoring=scoring, scoring_args=scoring_args) for order, seasonal_order in gen) # otherwise, we're fitting the stepwise algorithm... else: if n_samples < 10: start_p = min(start_p, 1) start_q = min(start_q, 1) start_P = start_Q = 0 # adjust to p, q, P, Q vals p = start_p = min(start_p, max_p) q = start_q = min(start_q, max_q) P = start_P = min(start_P, max_P) Q = start_Q = min(start_Q, max_Q) # init the stepwise model wrapper stepwise_wrapper = _StepwiseFitWrapper( y, xreg=exogenous, start_params=start_params, trend=trend, method=method, transparams=transparams, solver=solver, maxiter=maxiter, disp=disp, callback=callback, fit_params=fit_args, suppress_warnings=suppress_warnings, trace=trace, error_action=error_action, out_of_sample_size=out_of_sample_size, scoring=scoring, scoring_args=scoring_args, p=p, d=d, q=q, P=P, D=D, Q=Q, m=m, start_p=start_p, start_q=start_q, start_P=start_P, start_Q=start_Q, max_p=max_p, max_q=max_q, max_P=max_P, max_Q=max_Q, seasonal=seasonal, information_criterion=information_criterion, max_order=max_order) # fit a baseline p, d, q model and then a null model stepwise_wrapper.fit_increment_k_cache_set(True) # p, d, q, P, D, Q stepwise_wrapper.fit_increment_k_cache_set( True, p=0, q=0, P=0, Q=0) # null model # fit a basic AR model stepwise_wrapper.fit_increment_k_cache_set( max_p > 0 or max_P > 0, p=int(max_p > 0), q=0, P=int(m > 1 and max_P > 0), Q=0) # fit a basic MA model now stepwise_wrapper.fit_increment_k_cache_set( max_q > 0 or max_Q > 0, p=0, q=int(max_q > 0), P=0, Q=int(m > 1 and max_Q > 0)) # might not be 4 if p, q etc. are already 0 # assert stepwise_wrapper.k == 4 # sanity check... # do the step-through... all_res = stepwise_wrapper.step_through() # filter the non-successful ones filtered = _post_ppc_arima(all_res) # sort by the criteria - lower is better for both AIC and BIC # (https://stats.stackexchange.com/questions/81427/aic-guidelines-in-model-selection) sorted_res = sorted(filtered, key=(lambda mod: getattr(mod, information_criterion)())) # remove all the cached .pmdpkl files... someday write this as an exit hook # in case of a KeyboardInterrupt or anything for model in sorted_res: model._clear_cached_state() return _return_wrapper(sorted_res, return_valid_fits, start, trace)
class _StepwiseFitWrapper(object): """The stepwise algorithm fluctuates the more sensitive pieces of the ARIMA (the seasonal components) first, adjusting towards the direction of the smaller {A|B|HQ}IC(c), and continues to step down as long as the error shrinks. As long as the error term decreases and the best parameters have not shifted to a point where they can no longer change, k will increase, and the models will continue to be fit until the max_k is reached. References ---------- .. [1] R's auto-arima stepwise source code: http://bit.ly/2vOma0W """ def __init__(self, y, xreg, start_params, trend, method, transparams, solver, maxiter, disp, callback, fit_params, suppress_warnings, trace, error_action, out_of_sample_size, scoring, scoring_args, p, d, q, P, D, Q, m, start_p, start_q, start_P, start_Q, max_p, max_q, max_P, max_Q, seasonal, information_criterion, max_order): # todo: I really hate how congested this block is, and just for the # sake of a stateful __init__... Could we just pass **kwargs # (MUCH less expressive...) in here? It would be much more # difficult to debug later... # stuff for the fit call self.y = y self.xreg = xreg self.start_params = start_params self.trend = trend self.method = method self.transparams = transparams self.solver = solver self.maxiter = maxiter self.disp = disp self.callback = callback self.fit_params = fit_params self.suppress_warnings = suppress_warnings self.trace = trace self.error_action = error_action self.out_of_sample_size = out_of_sample_size self.scoring = scoring self.scoring_args = scoring_args self.information_criterion = information_criterion # order stuff self.p = p self.d = d self.q = q self.P = P self.D = D self.Q = Q self.m = m self.start_p = start_p self.start_q = start_q self.start_P = start_P self.start_Q = start_Q self.max_p = max_p self.max_q = max_q self.max_P = max_P self.max_Q = max_Q self.seasonal = seasonal self.max_order = max_order # other internal start vars self.bestfit = None self.k = self.start_k = 0 self.max_k = 100 # results list to store intermittent hashes of orders to determine if # we've seen this order before... self.results_dict = dict() # type: dict[tuple, ARIMA] # define the info criterion getter ONCE to avoid multiple lambda # creation calls self.get_ic = (lambda mod: getattr(mod, self.information_criterion)()) def is_new_better(self, new_model): if self.bestfit is None: return True elif new_model is None: return False current_ic, new_ic = self.get_ic(self.bestfit), self.get_ic(new_model) return new_ic < current_ic # this function takes a boolean expression, fits & caches a model, # increments k, and then sets the new value for p, d, P, Q, etc. def fit_increment_k_cache_set(self, expr, p=None, q=None, P=None, Q=None): # extract p, q, P, Q p = self.p if p is None else p q = self.q if q is None else q P = self.P if P is None else P Q = self.Q if Q is None else Q order = (p, self.d, q) ssnl = (P, self.D, Q, self.m) if self.seasonal else None # if the sum of the orders > max_order we do not build this model... order_sum = p + q + (P + Q if self.seasonal else 0) # if all conditions hold true, good to build this model. if expr and order_sum <= self.max_order and (order, ssnl) not \ in self.results_dict: self.k += 1 # cache the fit fit = _fit_arima(self.y, xreg=self.xreg, order=order, seasonal_order=ssnl, start_params=self.start_params, trend=self.trend, method=self.method, transparams=self.transparams, solver=self.solver, maxiter=self.maxiter, disp=self.disp, callback=self.callback, fit_params=self.fit_params, suppress_warnings=self.suppress_warnings, trace=self.trace, error_action=self.error_action, out_of_sample_size=self.out_of_sample_size, scoring=self.scoring, scoring_args=self.scoring_args) # use the orders as a key to be hashed for # the dictionary (pointing to fit) self.results_dict[(order, ssnl)] = fit if self.is_new_better(fit): self.bestfit = fit # reset p, d, P, Q, etc. self.p, self.q, self.P, self.Q = p, q, P, Q def step_through(self): while self.start_k < self.k < self.max_k: self.start_k = self.k # Each of these fit the models for an expression, a new p, # q, P or Q, and then reset best models # This takes the place of a lot of copy/pasted code.... # P fluctuations: self.fit_increment_k_cache_set(self.P > 0, P=self.P - 1) self.fit_increment_k_cache_set(self.P < self.max_P, P=self.P + 1) # Q fluctuations: self.fit_increment_k_cache_set(self.Q > 0, Q=self.Q - 1) self.fit_increment_k_cache_set(self.Q < self.max_Q, Q=self.Q + 1) # P & Q fluctuations: self.fit_increment_k_cache_set(self.Q > 0 and self.P > 0, P=self.P - 1, Q=self.Q - 1) self.fit_increment_k_cache_set(self.Q < self.max_Q and self.P < self.max_P, P=self.P + 1, Q=self.Q + 1) # p fluctuations: self.fit_increment_k_cache_set(self.p > 0, p=self.p - 1) self.fit_increment_k_cache_set(self.p < self.max_p, p=self.p + 1) # q fluctuations: self.fit_increment_k_cache_set(self.q > 0, q=self.q - 1) self.fit_increment_k_cache_set(self.q < self.max_q, q=self.q + 1) # p & q fluctuations: self.fit_increment_k_cache_set(self.p > 0 and self.q > 0, q=self.q - 1, p=self.p - 1) self.fit_increment_k_cache_set(self.q < self.max_q and self.p < self.max_p, q=self.q + 1, p=self.p + 1) # return the values return self.results_dict.values() def _fit_arima(x, xreg, order, seasonal_order, start_params, trend, method, transparams, solver, maxiter, disp, callback, fit_params, suppress_warnings, trace, error_action, out_of_sample_size, scoring, scoring_args): start = time.time() try: fit = ARIMA(order=order, seasonal_order=seasonal_order, start_params=start_params, trend=trend, method=method, transparams=transparams, solver=solver, maxiter=maxiter, disp=disp, callback=callback, suppress_warnings=suppress_warnings, out_of_sample_size=out_of_sample_size, scoring=scoring, scoring_args=scoring_args)\ .fit(x, exogenous=xreg, **fit_params) # for non-stationarity errors or singular matrices, return None except (LinAlgError, ValueError) as v: if error_action == 'warn': warnings.warn(_fmt_warning_str(order, seasonal_order), ModelFitWarning) elif error_action == 'raise': # todo: can we do something more informative in case # the error is not on the pyramid side? raise v # if it's 'ignore' or 'warn', we just return None fit = None # do trace if trace: print('Fit ARIMA: %s; AIC=%.3f, BIC=%.3f, Fit time=%.3f seconds' % (_fmt_order_info(order, seasonal_order), fit.aic() if fit is not None else np.nan, fit.bic() if fit is not None else np.nan, time.time() - start if fit is not None else np.nan)) return fit def _fmt_order_info(order, seasonal_order): return 'order=(%i, %i, %i)%s' \ % (order[0], order[1], order[2], '' if seasonal_order is None else ' seasonal_order=(%i, %i, %i, %i)' % (seasonal_order[0], seasonal_order[1], seasonal_order[2], seasonal_order[3])) def _fmt_warning_str(order, seasonal_order): """This is just so we can test that the string will format even if we don't want the warnings in the tests """ return ('Unable to fit ARIMA for %s; data is likely non-stationary. ' '(if you do not want to see these warnings, run ' 'with error_action="ignore")' % _fmt_order_info(order, seasonal_order)) def _post_ppc_arima(a): """If there are no suitable models, raise a ValueError. Otherwise, return a. In the case that a is an iterable (i.e., it made it to the end of the function), this method will filter out the None values and assess whether the list is empty. Parameters ---------- a : ARIMA or iterable The list or ARIMAs, or an ARIMA """ # if it's a result of making it to the end, it will # be a list of ARIMA models. Filter out the Nones # (the failed models)... if is_iterable(a): a = [m for m in a if m is not None] # if the list is empty, or if it was an ARIMA and it's None if not a: # check for truthiness rather than None explicitly raise ValueError('Could not successfully fit ARIMA to input data. ' 'It is likely your data is non-stationary. Please ' 'induce stationarity or try a different ' 'range of model order params. If your data is ' 'seasonal, check the period (m) of the data.') # good to return return a def _return_wrapper(fits, return_all, start, trace): """If the user wants to get all of the models back, this will return a list of the ARIMA models, otherwise it will just return the model. If this is called from the end of the function, fits will already be a list. We *know* that if a function call makes it here, fits is NOT None or it would have thrown an exception in :func:_post_ppc_arima. Parameters ---------- fits : iterable or ARIMA The ARIMA(s) return_all : bool Whether to return all. """ # make sure it's an iterable if not is_iterable(fits): fits = [fits] # whether to print the final runtime if trace: print('Total fit time: %.3f seconds' % (time.time() - start)) # which to return? if not all, then first index (assume sorted) if not return_all: return fits[0] return fits