Time Series analysis tsa statsmodels.tsa contains model classes and functions that are useful for time series analysis. This currently includes univariate autoregressive models (AR), vector autoregressive models (VAR) and univariate autoregressive moving average models (ARMA). It also includes descriptive statistics for time series, for example autocorrelation, partial autocorrelation function and periodogram, as well as the corresponding theoretical properties of ARMA or related processes. It

statsmodels.discrete.discrete_model.BinaryModel.pdf BinaryModel.pdf(X) The probability density (mass) function of the model.

statsmodels.tsa.ar_model.ARResults.wald_test ARResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. 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 as a string. See the examples. tuple : A tuple of arrays in

statsmodels.tsa.arima_model.ARMA.loglike_kalman ARMA.loglike_kalman(params, set_sigma2=True) [source] Compute exact loglikelihood for ARMA(p,q) model by the Kalman Filter.

statsmodels.sandbox.stats.multicomp.GroupsStats class statsmodels.sandbox.stats.multicomp.GroupsStats(x, useranks=False, uni=None, intlab=None) [source] statistics by groups (another version) groupstats as a class with lazy evaluation (not yet - decorators are still missing) written this time as equivalent of scipy.stats.rankdata gs = GroupsStats(X, useranks=True) assert_almost_equal(gs.groupmeanfilter, stats.rankdata(X[:,0]), 15) TODO: incomplete doc strings Methods groupdemean() groupss

statsmodels.sandbox.stats.multicomp.GroupsStats.runbasic_old GroupsStats.runbasic_old(useranks=False) [source]

statsmodels.graphics.gofplots.qqplot statsmodels.graphics.gofplots.qqplot(data, dist=, distargs=(), a=0, loc=0, scale=1, fit=False, line=None, ax=None) [source] Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution. Can take arguments specifying the parameters for dist or fit them automatically. (See fit under Parameters.) Parameters: data : array-like 1d data array dist : A scipy.stats or statsmodels distribution Compare x against dist. The default is scipy.stats.dis

statsmodels.discrete.discrete_model.BinaryResults.bse static BinaryResults.bse()

statsmodels.genmod.generalized_linear_model.GLMResults.llf static GLMResults.llf() [source]

statsmodels.sandbox.distributions.extras.ACSkewT_gen class statsmodels.sandbox.distributions.extras.ACSkewT_gen [source] univariate Skew-T distribution of Azzalini class follows scipy.stats.distributions pattern but with __init__ Methods cdf(x, *args, **kwds) Cumulative distribution function of the given RV. entropy(*args, **kwds) Differential entropy of the RV. est_loc_scale(*args, **kwds) est_loc_scale is deprecated! expect([func, args, loc, scale, lb, ub, ...]) Calculate expected val