DjddlmZddlZddlZddlmZddlm Z ddl m Z m Z ddl mZdd lmZdd lmZgd Zej(ej*ej,d Zej0ej2d ZdZddZddZy)) column_or_1dN)context_managers) get_callable)diff check_endog)DTYPE) stationarity) seasonality) is_constantndiffsnsdiffs)kpssadfpp)ocsbchcDt|}||dk(jS)aTest ``x`` for constancy. Determine whether a vector is composed of all of the same elements and nothing else. Parameters ---------- x : array-like, shape=(n_samples,) The time series vector. Examples -------- >>> import numpy as np >>> x = np.array([1, 2, 3]) >>> y = np.ones(3) >>> [is_constant(x), is_constant(y)] [False, True] r)rall)xs XC:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\pmdarima/arima/utils.pyr r %s"& QA 1I?? c |dkr tdt|t|fi|j}t |t d}t |ryd}||}|dk(rk||krf|dz }t||}t |r|St||kr&tjdt|||fz|S||}|dk(r||krf|S)aEstimate the seasonal differencing term, ``D``. Perform a test of seasonality for different levels of ``D`` to estimate the number of seasonal differences required to make a given time series stationary. Will select the maximum value of ``D`` for which the time series is judged seasonally stationary by the statistical test. Parameters ---------- x : array-like, shape=(n_samples, [n_features]) The array to difference. m : int The number of seasonal periods (i.e., frequency of the time series) max_D : int, optional (default=2) Maximum number of seasonal differences allowed. Must be a positive integer. The estimated value of ``D`` will not exceed ``max_D``. test : str, optional (default='ocsb') Type of unit root test of seasonality to use in order to detect seasonal periodicity. Valid tests include ("ocsb", "ch"). Note that the CHTest is very slow for large data. Returns ------- D : int The estimated seasonal differencing term. This is the maximum value of ``D`` such that ``D <= max_D`` and the time series is judged seasonally stationary. If the time series is constant, will return 0. rz max_D must be a positive integerFdtypecopyr )lagz}Appropriate D value may not have been reached; length of seasonally-differenced array (%i) is shorter than m (%i). Using D=%i) ValueErrorr VALID_STESTS#estimate_seasonal_differencing_termrr r rlenwarningswarn)rmmax_DtestkwargstestfuncDdodiffs rrr<sD z;<<0|D,/H q6A: MM<!VQN+ ,H!! A+!e)$ Hrc t|dkr tdt|t|fi|j}t |t d}d}t |r|Stjtjjtd|t|fz5||\}}tj|r dddy|ra||kr\|dz }t|}t |r |cdddS||\}}tj|r|dz cdddS|r||kr\ddd|S#1swY|SxYw) aEstimate ARIMA differencing term, ``d``. Perform a test of stationarity for different levels of ``d`` to estimate the number of differences required to make a given time series stationary. Will select the maximum value of ``d`` for which the time series is judged stationary by the statistical test. Parameters ---------- x : array-like, shape=(n_samples, [n_features]) The array (time series) to difference. alpha : float, optional (default=0.05) Level of the test. This is the value above below which the P-value will be deemed significant. test : str, optional (default='kpss') Type of unit root test of stationarity to use in order to test the stationarity of the time-series. One of ('kpss', 'adf', 'pp') max_d : int, optional (default=2) Maximum number of non-seasonal differences allowed. Must be a positive integer. The estimated value of ``d`` will not exceed ``max_d``. Returns ------- d : int The estimated differencing term. This is the maximum value of ``d`` such that ``d <= max_d`` and the time series is judged stationary. If the time series is constant, will return 0. References ---------- .. [1] R's auto_arima ndiffs function https://github.com/robjhyndman/forecast/blob/19b0711e554524bf6435b7524517715658c07699/R/arima.R#L132 # noqa: E501 rz max_d must be a positive integerFra Encountered exception in stationarity test (%r). This can occur in seasonal settings when a large enough `m` coupled with a large enough `D` difference the training array into too few samples for OLS (input contains %i samples). Try fitting on a larger training size) raise_err raise_msgNr )rr VALID_TESTS should_diffrr r ctxexcept_and_reraisenplinalg LinAlgErrorr"isnanr) ralphar'max_dr(r)dpvalr+s rrrsCL z;<</|D+.u??KKHAU/A A1~   II ! ! & *.s1v 7  { f 88D>$U FAQA1~16$A;LD&xx~1u?$U%D HED Hs"D-#D--$D-D--D7)rr)g?rr)sklearn.utils.validationrnumpyr3r#rr1utilsr utils.arrayrr compat.numpyr r statest_libr seatest_lib__all__KPSSTestADFTestPPTestr/OCSBTestCHTestr r rrrrrJsz2& + )(              .A HT r