M/Ph!ddlmZddlmZmZmZddlZddlZddlZ ddl m Z m Z m Z mZmZmZmZmZmZmZddlmZddlmZddlmcmZddlmcmZddlm Z dZ!d Z"ej#Z$ej%Z%d Z&d Z' dddZ(Gddej)Z*Gddej+Z,Gddej-Z.ej/e.e,dS)) annotations)is_float_index is_int_indexis_numeric_dtypeN) DatetimeIndexIndexPeriod PeriodIndex RangeIndexSeries Timestamp date_range period_range to_datetime) to_offset) PandasData) ValueWarninga %(model)s Parameters ---------- %(params)s dates : array_like, optional An array-like object of datetime objects. If a pandas object is given for endog or exog, it is assumed to have a DateIndex. freq : str, optional The frequency of the time-series. A Pandas offset or 'B', 'D', 'W', 'M', 'A', or 'Q'. This is optional if dates are given. %(extra_params)s %(extra_sections)szTimeseries model base classc |}|}t|ttf}t|}t|t}t |}t |}|rt|ttj frg|dkr | |kr||z}nT||dz krK |j }|j } n#t$r|j }|j} YnwxYw||dz| zz} t || | }|s||rz|sxt|ttj frW|dkr | |kr||z}nD||dkr8ttj|dt|dz}|r4|turt"} nt$} t|ttj frj|dkr| |kr |||z}n|t |dz kr2| |dt|dz|j}|d}n||}n|turt)||j} nt+|} | |dkrV| |d| |j}|d| ks)| |dt |dz|j}|d}|r||} n[|s|rB ||n5#t.t0f$r!}t3t5|d}~wwxYw|} n||} ||u}t| t6r | jdz }n| }| |d|dz|fS) a Get the location of a specific key in an index Parameters ---------- key : label The key for which to find the location if the underlying index is a DateIndex or a location if the underlying index is a RangeIndex or an Index with an integer dtype. index : pd.Index The index to search. Returns ------- loc : int The location of the key index : pd.Index The index including the key; this is a copy of the original index unless the index had to be expanded to accommodate `key`. index_was_expanded : bool Whether or not the index was expanded to accommodate `key`. Notes ----- If `key` is past the end of of the given index, and the index is either an Index with an integral dtype or a date index, this function extends the index up to and including key, and then returns the location in the new index. r)startstopstep)rperiodsfreq)rrendrN) isinstancer rrr typelenintnpintegerrrAttributeError_start_steprarangerrrr r get_loc IndexError ValueErrorKeyErrorstrslicer)keyindex base_index date_index int_index range_index index_classnobsbase_index_startbase_index_steprindex_fndate_keyloceindex_was_expandedrs ^/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/tsa/base/tsa_model.py get_index_locr>5s<J EJm(DEEJZ((IZ44Kz""K u::Dz#RZ'899 77tt||*CC 4!8^^ 3#-#3 ",/! 3 3 3#-#4 ","2 3$sQw/&AAD&TE  B  B B sS"*- . . B 77tt||*CC :b> ! !")JqM3sQw<<@@AAE/ - ' '!HH#H cC, - -( QwwC4$;;D3J's:*** $Q-aLL# BiCjk))!#JO<<<$S>>*R.(( $Q-XJO RyH,,$H(m #E Q'_EBi!mmC   !k! # #JJJJ' # # #3q66"" " #mmC  j0#uhl iai "4 44s*#B22C  C ?KK:K55K:c t||\}}}n#t$r} t|ttjfs||}nt|tr|j}t|tj r-|j tkrt j |}n|d}t|tjs|d|dz}d}n#|xYwYd}~nd}~wwxYw|||fS)a Get the location of a specific key in an index or model row labels Parameters ---------- key : label The key for which to find the location if the underlying index is a DateIndex or is only being used as row labels, or a location if the underlying index is a RangeIndex or a NumericIndex. index : pd.Index The index to search. row_labels : pd.Index Row labels to search if key not found in index Returns ------- loc : int The location of the key index : pd.Index The index including the key; this is a copy of the original index unless the index had to be expanded to accommodate `key`. index_was_expanded : bool Whether or not the index was expanded to accommodate `key`. Notes ----- This function expands on `get_index_loc` by first trying the given base index (or the model's index if the base index was not given) and then falling back to try again with the model row labels as the base index. rNrF)r>r+rr!r"r#r(r-rndarraydtypeboolargmaxnumbersIntegral)r.r/ row_labelsr:r<r;s r=get_index_label_locrGs5@%)6sE)B)B&U&& ###" cC#455  ((-- #u%% i#rz** !9$$ )C..CCa&Cc7#344 yqy)E!&   G     C#H ) ))s' C9CC('C4(C,,C44C9Freturn"tuple[int, int, int, Index | None]c < t|||j\}} } n#t$rtdwxYw| t|t |dz } t|||j\}} } n#t$rtdwxYwt |t r|j}t |t r |jdz }| |d} ||krtd|rt | t |kstdt |ts|stj dtd t|} na|r[|sY|j| s| s|j||dz} n?|stj d td tj d td n|rd} | "| d |_| d |_| |_nd|_d|_d|_t||dz z d }||z}|||| fS)a) Get the location of a specific key in an index or model row labels Parameters ---------- start : label The key at which to start prediction. Depending on the underlying model's index, may be an integer, a date (string, datetime object, pd.Timestamp, or pd.Period object), or some other object in the model's row labels. end : label The key at which to end prediction (note that this key will be *included* in prediction). Depending on the underlying model's index, may be an integer, a date (string, datetime object, pd.Timestamp, or pd.Period object), or some other object in the model's row labels. nobs : int base_index : pd.Index index : pd.Index, optional Optionally an index to associate the predicted results to. If None, an attempt is made to create an index for the predicted results from the model's index or model's row labels. silent : bool, optional Argument to silence warnings. Returns ------- start : int The index / observation location at which to begin prediction. end : int The index / observation location at which to end in-sample prediction. The maximum value for this is nobs-1. out_of_sample : int The number of observations to forecast after the end of the sample. prediction_index : pd.Index or None The index associated with the prediction results. This index covers the range [start, end + out_of_sample]. If the model has no given index and no given row labels (i.e. endog/exog is not Pandas), then this will be None. Notes ----- The arguments `start` and `end` behave differently, depending on if they are integer or not. If either is an integer, then it is assumed to refer to a *location* in the index, not to an index value. On the other hand, if it is a date string or some other type of object, then it is assumed to refer to an index *value*. In all cases, the returned `start` and `end` values refer to index *locations* (so in the former case, the given location is validated and returned whereas in the latter case a location is found that corresponds to the given index value). This difference in behavior is necessary to support `RangeIndex`. This is because integers for a RangeIndex could refer either to index values or to index locations in an ambiguous way (while for `NumericIndex`, since we have required them to be full indexes, there is no ambiguity). zYThe `start` argument could not be matched to a location related to the index of the data.NrzWThe `end` argument could not be matched to a location related to the index of the data.z)Prediction must have `end` after `start`.zeInvalid `index` provided in prediction. Must have length consistent with `start` and `end` arguments.zBecause the model data (`endog`, `exog`) were not given as Pandas objects, the prediction output will be Numpy arrays, and the given `index` argument will only be used internally. stacklevelzmNo supported index is available. Prediction results will be given with an integer index beginning at `start`.zNo supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.rr)rGrFr+maxr rr-rrr*rwarningswarnrr FutureWarning predict_start predict_end predict_dates)rrr5r0r/silent index_noneindex_generateddata_ start_oos end_indexend_oosprediction_index out_of_samples r=get_prediction_indexr_sP 1 :t  q))     :     {%Z1,-- "5 T_# # Y     :    % #uhl!( U{{DEEE  #$$E 22( $ ++ F  M     !<<   ? & &W &#usQw?    >!  M        #-a0+B/-!!tax(!,,M=C #}&6 66s7A88BceZdZeeeeddzZ dfd ZddZ ddZ ddZ ddd Z d Z dZee eddZxZS)TimeSeriesModel)modelparams extra_paramsextra_sectionsNnonec ntj||fd|i||||dS)Nmissing)super__init__ _init_dates)selfendogexogdatesrrikwargs __class__s r=rkzTimeSeriesModel.__init__sI @@g@@@@ %%%%%c||}n |jj}||tdd}|t|tt fs t j|}t|s*t|st|dtrtdt|tr|j }t|}t|tstd|}n#|tdYnxYwt|tt fr|3|j,|j}|#d}|t#jd |zt&d |t)|}||j|td n|Q|jJt+|d|d |}|s'||kstd|}n0||s|j|kstdn|td|du}t|tt f}t|t } t/|} t|t0} |r |jdund} tt3|jjd} | r|| nd}|r |j}n#t<$r |j}YnwxYwd}|r"|s | s|st#jdt&d |r| st#jdt&d |r|st#jdt&d d}|r| r|p| o|p| }|r|}n| }d}||_ ||_!|du|_"| o| o| |_#|o| |_$|j$r |j jnd|_%||_&|j$r|j nd|j_'|j$r |j j(nd|j_dS)a Initialize dates Parameters ---------- dates : array_like, optional An array like object containing dates. freq : str, tuple, datetime.timedelta, DateOffset or None, optional A frequency specification for either `dates` or the row labels from the endog / exog data. Notes ----- Creates `self._index` and related attributes. `self._index` is always a Pandas index, and it is always NumericIndex, DatetimeIndex, or PeriodIndex. If Pandas objects, endog / exog may have any type of index. If it is an NumericIndex with values 0, 1, ..., nobs-1 or if it is (coerceable to) a DatetimeIndex or PeriodIndex *with an associated frequency*, then it is called a "supported" index. Otherwise it is called an "unsupported" index. Supported indexes are standardized (i.e. a list of date strings is converted to a DatetimeIndex) and the result is put in `self._index`. Unsupported indexes are ignored, and a supported NumericIndex is generated and put in `self._index`. Warnings are issued in this case to alert the user if the returned index from some operation (e.g. forecasting) is different from the original data's index. However, whenever possible (e.g. purely in-sample prediction), the original index is returned. The benefit of supported indexes is that they allow *forecasting*, i.e. it is possible to extend them in a reasonable way. Thus every model must have an underlying supported index, even if it is just a generated NumericIndex. Nz,Frequency provided without associated index.FrzNumeric index givenzCould not coerce to date indexz2Non-date index index provided to `dates` argument.TzMNo frequency information was provided, so inferred frequency %s will be used.rKrLzYNo frequency information was provided with date index and no frequency could be inferred.rrzEThe given frequency argument could not be matched to the given index.zBThe given frequency argument is incompatible with the given index.zKGiven index could not be coerced to dates but `freq` argument was provided.zAn unsupported index was provided. As a result, forecasts cannot be generated. To use the model for forecasting, use one of the supported classes of index.z|A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.zeA date index has been provided, but it is not monotonic and so will be ignored when e.g. forecasting.))rXrFr*rrr r"asarrayrrfloatr valuesrrr inferred_freqrOrPrrrallrr rangernshapeequalsis_monotonic_increasingr$ is_monotonic_index_index_generated _index_none _index_int64 _index_dates _index_freq_index_inferred_freqrpfreqstr)rmrprr/rxrresampled_index has_indexr1 period_indexr2r3has_freq increment is_incrementr~rW valid_indexs r=rlzTimeSeriesModel._init_datess3T  EEI(E =T-KLL L   em[%ABB* ) Z..F(00@)%00@'vay%88@ ))>??? "%00- % )//F&fe44K()IJJJ"EE  ((1)(%-!=>>>  ! 9 %  {'CDD !%55  ''   33 -7A5:T))T% 0 34455 2;Eu||I...   2$< ! 2 2 2$1  2 L  j K <  M5       h  M2       l  M         5H 5 *l    #FFF"O / D=%Lk/L*n&>+>/3/@J4;++d$1!*.):D$++ 040AK,,t sB-C55D ;KKKc4||j}t||S)a9 Get the location of a specific key in an index Parameters ---------- key : label The key for which to find the location if the underlying index is a DateIndex or a location if the underlying index is a RangeIndex or an NumericIndex. base_index : pd.Index, optional Optionally the base index to search. If None, the model's index is searched. Returns ------- loc : int The location of the key index : pd.Index The index including the key; this is a copy of the original index unless the index had to be expanded to accommodate `key`. index_was_expanded : bool Whether or not the index was expanded to accommodate `key`. Notes ----- If `key` is past the end of of the given index, and the index is either an NumericIndex or a date index, this function extends the index up to and including key, and then returns the location in the new index. )rr>rmr.r0s r=_get_index_loczTimeSeriesModel._get_index_locs!>  JS*---rscJ||j}t|||jjS)a| Get the location of a specific key in an index or model row labels Parameters ---------- key : label The key for which to find the location if the underlying index is a DateIndex or is only being used as row labels, or a location if the underlying index is a RangeIndex or an NumericIndex. base_index : pd.Index, optional Optionally the base index to search. If None, the model's index is searched. Returns ------- loc : int The location of the key index : pd.Index The index including the key; this is a copy of the original index unless the index had to be expanded to accommodate `key`. index_was_expanded : bool Whether or not the index was expanded to accommodate `key`. Notes ----- This method expands on `_get_index_loc` by first trying the given base index (or the model's index if the base index was not given) and then falling back to try again with the model row labels as the base index. )rrGrXrFrs r=_get_index_label_locz$TimeSeriesModel._get_index_label_locs(>  J"3 DI4HIIIrsFrHrIc t|j}t||||j|||j|j|j S)a Get the location of a specific key in an index or model row labels Parameters ---------- start : label The key at which to start prediction. Depending on the underlying model's index, may be an integer, a date (string, datetime object, pd.Timestamp, or pd.Period object), or some other object in the model's row labels. end : label The key at which to end prediction (note that this key will be *included* in prediction). Depending on the underlying model's index, may be an integer, a date (string, datetime object, pd.Timestamp, or pd.Period object), or some other object in the model's row labels. index : pd.Index, optional Optionally an index to associate the predicted results to. If None, an attempt is made to create an index for the predicted results from the model's index or model's row labels. silent : bool, optional Argument to silence warnings. Returns ------- start : int The index / observation location at which to begin prediction. end : int The index / observation location at which to end in-sample prediction. The maximum value for this is nobs-1. out_of_sample : int The number of observations to forecast after the end of the sample. prediction_index : pd.Index or None The index associated with the prediction results. This index covers the range [start, end + out_of_sample]. If the model has no given index and no given row labels (i.e. endog/exog is not Pandas), then this will be None. Notes ----- The arguments `start` and `end` behave differently, depending on if they are integer or not. If either is an integer, then it is assumed to refer to a *location* in the index, not to an index value. On the other hand, if it is a date string or some other type of object, then it is assumed to refer to an index *value*. In all cases, the returned `start` and `end` values refer to index *locations* (so in the former case, the given location is validated and returned whereas in the latter case a location is found that corresponds to the given index value). This difference in behavior is necessary to support `RangeIndex`. This is because integers for a RangeIndex could refer either to index values or to index locations in an ambiguous way (while for `NumericIndex`, since we have required them to be full indexes, there is no ambiguity). )r0r/rUrVrWrX)r rnr_rrrrX)rmrrr/rUr5s r=_get_prediction_indexz%TimeSeriesModel._get_prediction_index sNp4:#   {' 1    rsc|jjSN)rXxnames)rms r=_get_exog_nameszTimeSeriesModel._get_exog_namesQs yrscNt|ts|g}||j_dSr)rlistrXr)rmvalss r=_set_exog_nameszTimeSeriesModel._set_exog_namesTs+$%% 6D rsz%The names of the exogenous variables.)NNNrg)NNr)NFrHrI)__name__ __module__ __qualname___tsa_doc _model_doc_generic_params_missing_param_doc__doc__rkrlrrrrrproperty exog_names __classcell__rrs@r=raras!* G@F&&&&&&iLiLiLiLV!.!.!.!.F!J!J!J!JFC C C C C J       / JJJJJrsrac eZdZdfd ZxZS)TimeSeriesModelResults?ch|j|_t||||dSr)rXrjrk)rmrcrdnormalized_cov_paramsscalerrs r=rkzTimeSeriesModelResults.__init__ds1J  (=uEEEEErs)r)rrrrkrrs@r=rrcsGFFFFFFFFFFrsrceZdZiZejejjeZddiZ ejejj e Z dS)TimeSeriesResultsWrapperpredictrpN) rrr_attrswrap union_dictsbaseLikelihoodResultsWrapper _wrap_attrs_methods _wrap_methodsrsr=rris] F"$" %16K7#H$D$ %3XMMMrsr)NFFNNr)0 __future__rstatsmodels.compat.pandasrrrrDrOnumpyr"pandasrrr r r r r rrrpandas.tseries.frequenciesrstatsmodels.base.datarstatsmodels.base.modelrrcstatsmodels.base.wrapperwrapperrstatsmodels.tools.sm_exceptionsrrr_model_params_docrrr>rGr_LikelihoodModelraLikelihoodModelResultsrResultsWrapperrpopulate_wrapperrrsr=rs""""""                         100000,,,,,,%%%%%%%%%'''''''''888888 + (,\5\5\5~F*F*F*\   k7k7k7k7k7\UUUUUd*UUUp FFFFFT8FFF t24rs