statsmodels.tsa.filters.filtertools.fftconvolve3 statsmodels.tsa.filters.filtertools.fftconvolve3(in1, in2=None, in3=None, mode='full') [source] Convolve two N-dimensional arrays using FFT. See convolve. for use with arma (old version: in1=num in2=den in3=data better for consistency with other functions in1=data in2=num in3=den note in2 and in3 need to have consistent dimension/shape since I?m using max of in2, in3 shapes and not the sum copied from scipy.signal.signaltools, but here used to

statsmodels.tsa.filters.filtertools.convolution_filter statsmodels.tsa.filters.filtertools.convolution_filter(x, filt, nsides=2) [source] Linear filtering via convolution. Centered and backward displaced moving weighted average. Parameters: x : array_like data array, 1d or 2d, if 2d then observations in rows filt : array_like Linear filter coefficients in reverse time-order. Should have the same number of dimensions as x though if 1d and x is 2d will be coerced to 2d. nsides : int, opti

statsmodels.tsa.filters.cf_filter.cffilter statsmodels.tsa.filters.cf_filter.cffilter(X, low=6, high=32, drift=True) [source] Christiano Fitzgerald asymmetric, random walk filter Parameters: X : array-like 1 or 2d array to filter. If 2d, variables are assumed to be in columns. low : float Minimum period of oscillations. Features below low periodicity are filtered out. Default is 6 for quarterly data, giving a 1.5 year periodicity. high : float Maximum period of oscillations. Features a

statsmodels.tsa.filters.bk_filter.bkfilter statsmodels.tsa.filters.bk_filter.bkfilter(X, low=6, high=32, K=12) [source] Baxter-King bandpass filter Parameters: X : array-like A 1 or 2d ndarray. If 2d, variables are assumed to be in columns. low : float Minimum period for oscillations, ie., Baxter and King suggest that the Burns-Mitchell U.S. business cycle has 6 for quarterly data and 1.5 for annual data. high : float Maximum period for oscillations BK suggest that the U.S. business cy

statsmodels.tsa.ar_model.ARResults class statsmodels.tsa.ar_model.ARResults(model, params, normalized_cov_params=None, scale=1.0) [source] Class to hold results from fitting an AR model. Parameters: model : AR Model instance Reference to the model that is fit. params : array The fitted parameters from the AR Model. normalized_cov_params : array inv(dot(X.T,X)) where X is the lagged values. scale : float, optional An estimate of the scale of the model. Returns: **Attributes** : aic

statsmodels.tsa.ar_model.AR class statsmodels.tsa.ar_model.AR(endog, dates=None, freq=None, missing='none') [source] Autoregressive AR(p) model Parameters: endog : array-like 1-d endogenous response variable. The independent variable. dates : array-like of datetime, optional An array-like object of datetime objects. If a pandas object is given for endog or exog, it is assumed to have a DateIndex. freq : str, optional The frequency of the time-series. A Pandas offset or ?B?, ?D?, ?W?, ?

statsmodels.tsa.arima_process.lpol_sdiff statsmodels.tsa.arima_process.lpol_sdiff(s) [source] return coefficients for seasonal difference (1-L^s) just a trivial convenience function Parameters: s : int number of periods in season Returns: sdiff : list, length s+1

statsmodels.tsa.arima_process.lpol_fima statsmodels.tsa.arima_process.lpol_fima(d, n=20) [source] MA representation of fractional integration Parameters: d : float fractional power n : int number of terms to calculate, including lag zero Returns: ma : array coefficients of lag polynomial

statsmodels.tsa.arima_process.lpol_fiar statsmodels.tsa.arima_process.lpol_fiar(d, n=20) [source] AR representation of fractional integration Parameters: d : float fractional power n : int number of terms to calculate, including lag zero Returns: ar : array coefficients of lag polynomial Notes: : first coefficient is 1, negative signs except for first term, : ar(L)*x_t :

statsmodels.tsa.arima_process.lpol2index statsmodels.tsa.arima_process.lpol2index(ar) [source] remove zeros from lagpolynomial, squeezed representation with index Parameters: ar : array_like coefficients of lag polynomial Returns: coeffs : array non-zero coefficients of lag polynomial index : array index (lags) of lagpolynomial with non-zero elements