statsmodels.tsa.arima_model.ARIMA.fit ARIMA.fit(start_params=None, trend='c', method='css-mle', transparams=True, solver='lbfgs', maxiter=50, full_output=1, disp=5, callback=None, **kwargs) [source] Fits ARIMA(p,d,q) model by exact maximum likelihood via Kalman filter. Parameters: start_params : array-like, optional Starting parameters for ARMA(p,q). If None, the default is given by ARMA._fit_start_params. See there for more information. transparams : bool, optional Whehter or not to tra

statsmodels.tsa.ar_model.AR.select_order AR.select_order(maxlag, ic, trend='c', method='mle') [source] Select the lag order according to the information criterion. Parameters: maxlag : int The highest lag length tried. See AR.fit. ic : str {?aic?,?bic?,?hqic?,?t-stat?} Criterion used for selecting the optimal lag length. See AR.fit. trend : str {?c?,?nc?} Whether to include a constant or not. ?c? - include constant. ?nc? - no constant. Returns: bestlag : int Best lag according to I

statsmodels.tsa.ar_model.AR.score AR.score(params) [source] Return the gradient of the loglikelihood at params. Parameters: params : array-like The parameter values at which to evaluate the score function. Notes Returns numerical gradient.

statsmodels.tsa.ar_model.AR.predict AR.predict(params, start=None, end=None, dynamic=False) [source] Returns in-sample and out-of-sample prediction. Parameters: params : array The fitted model parameters. start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first

statsmodels.tsa.ar_model.AR.loglike AR.loglike(params) [source] The loglikelihood of an AR(p) process Parameters: params : array The fitted parameters of the AR model Returns: llf : float The loglikelihood evaluated at params Notes Contains constant term. If the model is fit by OLS then this returns the conditonal maximum likelihood. If it is fit by MLE then the (exact) unconditional maximum likelihood is returned. where is a (p x 1) vector with each element equal to the mean

statsmodels.tsa.ar_model.AR.initialize AR.initialize() [source]

statsmodels.tsa.ar_model.AR.information AR.information(params) [source] Not Implemented Yet

statsmodels.tsa.ar_model.AR.hessian AR.hessian(params) [source] Returns numerical hessian for now.

statsmodels.tsa.ar_model.AR.from_formula classmethod AR.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args : extra arguments The

statsmodels.tsa.ar_model.AR.fit AR.fit(maxlag=None, method='cmle', ic=None, trend='c', transparams=True, start_params=None, solver='lbfgs', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the unconditional maximum likelihood of an AR(p) process. Parameters: maxlag : int If ic is None, then maxlag is the lag length used in fit. If ic is specified then maxlag is the highest lag order used to select the correct lag order. If maxlag is None, the default is round(12*(no