statsmodels.tsa.arima_model.ARMAResults.initialize ARMAResults.initialize(model, params, **kwd)

statsmodels.tsa.arima_model.ARMAResults.forecast ARMAResults.forecast(steps=1, exog=None, alpha=0.05) [source] Out-of-sample forecasts Parameters: steps : int The number of out of sample forecasts from the end of the sample. exog : array If the model is an ARMAX, you must provide out of sample values for the exogenous variables. This should not include the constant. alpha : float The confidence intervals for the forecasts are (1 - alpha) % Returns: forecast : array Array of out of

statsmodels.tsa.arima_model.ARMAResults.f_test ARMAResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given

statsmodels.tsa.arima_model.ARMAResults.cov_params ARMAResults.cov_params() [source]

statsmodels.tsa.arima_process.ArmaProcess.periodogram ArmaProcess.periodogram(nobs=None) [source] periodogram for ARMA process given by lag-polynomials ar and ma Parameters: ar : array_like autoregressive lag-polynomial with leading 1 and lhs sign ma : array_like moving average lag-polynomial with leading 1 worN : {None, int}, optional option for scipy.signal.freqz (read ?w or N?) If None, then compute at 512 frequencies around the unit circle. If a single integer, the compute at that

statsmodels.tsa.arima_model.ARMAResults.conf_int ARMAResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interval. ?Default?

statsmodels.tsa.arima_process.ArmaProcess.invertroots ArmaProcess.invertroots(retnew=False) [source] make MA polynomial invertible by inverting roots inside unit circle Parameters: retnew : boolean If False (default), then return the lag-polynomial as array. If True, then return a new instance with invertible MA-polynomial Returns: manew : array new invertible MA lag-polynomial, returned if retnew is false. wasinvertible : boolean True if the MA lag-polynomial was already invertible,

statsmodels.tsa.arima_process.ArmaProcess.impulse_response ArmaProcess.impulse_response(nobs=None) [source] get the impulse response function (MA representation) for ARMA process Parameters: ma : array_like, 1d moving average lag polynomial ar : array_like, 1d auto regressive lag polynomial nobs : int number of observations to calculate Returns: ir : array, 1d impulse response function with nobs elements Notes This is the same as finding the MA representation of an ARMA(p,q). By

statsmodels.tsa.arima_process.ArmaProcess.pacf ArmaProcess.pacf(nobs=None) [source] partial autocorrelation function of an ARMA process Parameters: ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag nobs : int number of terms (lags plus zero lag) to include in returned pacf Returns: pacf : array partial autocorrelation of ARMA process given by ar, ma Notes solv

statsmodels.tsa.arima_process.ArmaProcess.from_estimation classmethod ArmaProcess.from_estimation(model_results, nobs=None) [source] Create ArmaProcess instance from ARMA estimation results Parameters: model_results : ARMAResults instance A fitted model nobs : int, optional If None, nobs is taken from the results