statsmodels.tsa.stattools.arma_order_select_ic 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 po

statsmodels.tsa.stattools.adfuller statsmodels.tsa.stattools.adfuller(x, maxlag=None, regression='c', autolag='AIC', store=False, regresults=False) [source] Augmented Dickey-Fuller unit root test The Augmented Dickey-Fuller test can be used to test for a unit root in a univariate process in the presence of serial correlation. Parameters: x : array_like, 1d data series maxlag : int Maximum lag which is included in test, default 12*(nobs/100)^{1/4} regression : str {?c?,?ct?,?ctt?,?nc?}

statsmodels.tsa.stattools.acovf statsmodels.tsa.stattools.acovf(x, unbiased=False, demean=True, fft=False) [source] Autocovariance for 1D Parameters: x : array Time series data. Must be 1d. unbiased : bool If True, then denominators is n-k, otherwise n demean : bool If True, then subtract the mean x from each element of x fft : bool If True, use FFT convolution. This method should be preferred for long time series. Returns: acovf : array autocovariance function

statsmodels.tsa.stattools.acf statsmodels.tsa.stattools.acf(x, unbiased=False, nlags=40, qstat=False, fft=False, alpha=None) [source] Autocorrelation function for 1d arrays. Parameters: x : array Time series data unbiased : bool If True, then denominators for autocovariance are n-k, otherwise n nlags: int, optional : Number of lags to return autocorrelation for. qstat : bool, optional If True, returns the Ljung-Box q statistic for each autocorrelation coefficient. See q_stat for more

statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter class statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter [source] Kalman Filter code intended for use with the ARMA model. Notes The notation for the state-space form follows Durbin and Koopman (2001). The observation equations is The state equation is For the present purposed epsilon_{t} is assumed to always be zero. Methods R(params, r, k, q, p) The coefficient matrix for the state vector in the observation equation. T(params, r, k,

statsmodels.tsa.interp.denton.dentonm statsmodels.tsa.interp.denton.dentonm(indicator, benchmark, freq='aq', **kwargs) [source] Modified Denton?s method to convert low-frequency to high-frequency data. Uses proportionate first-differences as the penalty function. See notes. Parameters: indicator : A low-frequency indicator series. It is assumed that there are no pre-sample indicators. Ie., the first indicators line up with the first benchmark. benchmark : array-like The higher frequency

statsmodels.tsa.filters.hp_filter.hpfilter statsmodels.tsa.filters.hp_filter.hpfilter(X, lamb=1600) [source] Hodrick-Prescott filter Parameters: X : array-like The 1d ndarray timeseries to filter of length (nobs,) or (nobs,1) lamb : float The Hodrick-Prescott smoothing parameter. A value of 1600 is suggested for quarterly data. Ravn and Uhlig suggest using a value of 6.25 (1600/4**4) for annual data and 129600 (1600*3**4) for monthly data. Returns: cycle : array The estimated cycle i

statsmodels.tsa.filters.filtertools.recursive_filter statsmodels.tsa.filters.filtertools.recursive_filter(x, ar_coeff, init=None) [source] Autoregressive, or recursive, filtering. Parameters: x : array-like Time-series data. Should be 1d or n x 1. ar_coeff : array-like AR coefficients in reverse time order. See Notes init : array-like Initial values of the time-series prior to the first value of y. The default is zero. Returns: y : array Filtered array, number of columns determined

statsmodels.tsa.filters.filtertools.miso_lfilter statsmodels.tsa.filters.filtertools.miso_lfilter(ar, ma, x, useic=False) [source] use nd convolution to merge inputs, then use lfilter to produce output arguments for column variables return currently 1d Parameters: ar : array_like, 1d, float autoregressive lag polynomial including lag zero, ar(L)y_t ma : array_like, same ndim as x, currently 2d moving average lag polynomial ma(L)x_t x : array_like, 2d input data series, time in rows, va

statsmodels.tsa.filters.filtertools.fftconvolveinv statsmodels.tsa.filters.filtertools.fftconvolveinv(in1, in2, mode='full') [source] Convolve two N-dimensional arrays using FFT. See convolve. copied from scipy.signal.signaltools, but here used to try out inverse filter doesn?t work or I can?t get it to work 2010-10-23: looks ok to me for 1d, from results below with padded data array (fftp) but it doesn?t work for multidimensional inverse filter (fftn) original signal.fftconvolve also uses f