srch

statsmodels.tsa.stattools.acf¶ statsmodels.tsa.stattools.acf (x, unbiased=False, nlags=40, qstat=False, fft=None, alpha=None, missing='none') [source] ¶ Calculate the autocorrelation function. Parameters x array_like. The time series data. unbiased bool. If True, then denominators for autocovariance are n-k, otherwise n.

statsmodels.tsa.stattools.acovf(x, adjusted=False, demean=True, fft=True, missing='none', nlag=None) [source] Estimate autocovariances. Parameters x array_like Time series data. Must be 1d. adjusted bool, default False If True, then denominators is n-k, otherwise n. demean bool, default True If True, then subtract the mean x from each element of x.

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 The data series to test.

statsmodels.tsa.stattools.adfuller(x, maxlag=None, regression='c', autolag='AIC', store=False, regresults=False)[source]¶. Augmented Dickey-Fuller unit root ...

python import shorthands. statsmodels.tsa.stattools. Imported 8 times. 8 × import statsmodels.tsa.stattools as ts. import as…

statsmodels.tsa.stattools.pacf_ols¶ ... Calculate partial autocorrelations via OLS. ... This solves a separate OLS estimation for each desired lag using method in [ ...

Source code for statsmodels.tsa.stattools. """ Statistical tools for time series analysis """ from __future__ import division from statsmodels.compat.python ...

See Also-----statsmodels.tsa.stattools.pacf Partial autocorrelation estimation. statsmodels.tsa.stattools.pacf_yw Partial autocorrelation estimation using Yule-Walker. statsmodels.tsa.stattools.pacf_burg Partial autocorrelation estimation using Burg"s method. Notes-----This solves a separate OLS estimation for each desired lag using method in [1]_.

Source code for statsmodels.tsa.stattools. """ Statistical tools for time series analysis """ import warnings from statsmodels.compat.numpy ...

statsmodels.tsa.stattools.coint¶ ... Test for no-cointegration of a univariate equation. The null hypothesis is no cointegration. Variables in y0 and y1 are ...

Source code for statsmodels.tsa.stattools. """ Statistical tools for time series analysis """ from __future__ import annotations from ...

statsmodels/statsmodels/tsa/stattools.py ... from statsmodels.tsa.adfvalues import mackinnoncrit, mackinnonp. from statsmodels.tsa.tsatools import add_trend ...

statsmodels.tsa.stattools.acf(x, adjusted=False, nlags=None, qstat=False, fft=True, alpha=None, bartlett_confint=True, missing='none')[source] Calculate the autocorrelation function. Parameters x array_like The time series data. adjusted bool, default False If True, then denominators for autocovariance are n-k, otherwise n. nlags int, optional

statsmodels.tsa.stattools.acf¶ · The time series data. adjusted · Number of lags to return autocorrelation for. · If True, returns the Ljung-Box q statistic for ...

statsmodels.tsa.stattools.q_stat(x, nobs)[source]¶. Compute Ljung-Box Q Statistic. Parameters. xarray_like. Array of autocorrelation coefficients.

statsmodels.tsa.stattools.kpss(x, regression='c', nlags='auto', store=False)[source] Kwiatkowski-Phillips-Schmidt-Shin test for stationarity. Computes the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test for the null hypothesis that x is level or trend stationary. Parameters x array_like, 1d The data series to test. regression str{“c”, “ct”}

statsmodels.tsa contains model classes and functions that are useful for time series analysis. Basic models include univariate autoregressive models (AR), vector autoregressive models (VAR) and univariate autoregressive moving average models (ARMA). Non-linear models include Markov switching dynamic regression and autoregression.