4.8.1. statsmodels.tsa.api

4.8.1.1. Functions

acf(x[, unbiased, nlags, qstat, fft, alpha]) Autocorrelation function for 1d arrays.
acovf(x[, unbiased, demean, fft]) Autocovariance for 1D
add_lag(x[, col, lags, drop, insert]) Returns an array with lags included given an array.
add_trend(X[, trend, prepend, has_constant]) Adds a trend and/or constant to an array.
adfuller(x[, maxlag, regression, autolag, ...]) Augmented Dickey-Fuller unit root test
arma_generate_sample(ar, ma, nsample[, ...]) Generate a random sample of an ARMA process
arma_order_select_ic(y[, max_ar, max_ma, ...]) Returns information criteria for many ARMA models
ccf(x, y[, unbiased]) cross-correlation function for 1d
ccovf(x, y[, unbiased, demean]) crosscovariance for 1D
coint(y1, y2[, regression]) This is a simple cointegration test.
detrend(x[, order, axis]) detrend an array with a trend of given order along axis 0 or 1
lagmat(x, maxlag[, trim, original]) create 2d array of lags
lagmat2ds(x, maxlag0[, maxlagex, dropex, trim]) generate lagmatrix for 2d array, columns arranged by variables
pacf(x[, nlags, method, alpha]) Partial autocorrelation estimated
pacf_ols(x[, nlags]) Calculate partial autocorrelations
pacf_yw(x[, nlags, method]) Partial autocorrelation estimated with non-recursive yule_walker
periodogram(X) Returns the periodogram for the natural frequency of X
q_stat(x, nobs[, type]) Return’s Ljung-Box Q Statistic
seasonal_decompose(x[, model, filt, freq])
Parameters:
x13_arima_analysis(endog[, maxorder, ...]) Perform x13-arima analysis for monthly or quarterly data.
x13_arima_select_order(endog[, maxorder, ...]) Perform automatic seaonal ARIMA order identification using x12/x13 ARIMA.

4.8.1.2. Classes

AR(endog[, dates, freq, missing]) Autoregressive AR(p) model
ARIMA(endog, order[, exog, dates, freq, missing]) Autoregressive Integrated Moving Average ARIMA(p,d,q) Model
ARMA(endog, order[, exog, dates, freq, missing]) Autoregressive Moving Average ARMA(p,q) Model
ArmaProcess(ar, ma[, nobs]) Represent an ARMA process for given lag-polynomials
DynamicVAR(data[, lag_order, window, ...]) Estimates time-varying vector autoregression (VAR(p)) using
SVAR(endog, svar_type[, dates, freq, A, B, ...]) Fit VAR and then estimate structural components of A and B, defined:
VAR(endog[, dates, freq, missing]) Fit VAR(p) process and do lag order selection