statsmodels.tsa.stattools.arma_order_select_ic¶
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statsmodels.tsa.stattools.
arma_order_select_ic
(y, max_ar=4, max_ma=2, ic='bic', trend='c', model_kw={}, fit_kw={})[source]¶ Returns information criteria for many ARMA models
Parameters: y : array-like
Time-series data
max_ar : int
Maximum number of AR lags to use. Default 4.
max_ma : int
Maximum number of MA lags to use. Default 2.
ic : str, list
Information criteria to report. Either a single string or a list of different criteria is possible.
trend : str
The trend to use when fitting the ARMA models.
model_kw : dict
Keyword arguments to be passed to the
ARMA
modelfit_kw : dict
Keyword arguments to be passed to
ARMA.fit
.Returns: obj : Results object
Each ic is an attribute with a DataFrame for the results. The AR order used is the row index. The ma order used is the column index. The minimum orders are available as
ic_min_order
.Notes
This method can be used to tentatively identify the order of an ARMA process, provided that the time series is stationary and invertible. This function computes the full exact MLE estimate of each model and can be, therefore a little slow. An implementation using approximate estimates will be provided in the future. In the meantime, consider passing {method : ‘css’} to fit_kw.
Examples
>>> from statsmodels.tsa.arima_process import arma_generate_sample >>> import statsmodels.api as sm >>> import numpy as np
>>> arparams = np.array([.75, -.25]) >>> maparams = np.array([.65, .35]) >>> arparams = np.r_[1, -arparams] >>> maparam = np.r_[1, maparams] >>> nobs = 250 >>> np.random.seed(2014) >>> y = arma_generate_sample(arparams, maparams, nobs) >>> res = sm.tsa.arma_order_select_ic(y, ic=['aic', 'bic'], trend='nc') >>> res.aic_min_order >>> res.bic_min_order