pysteps.timeseries#

Methods and models for time series analysis.

pysteps.timeseries.autoregression#

Methods related to autoregressive AR(p) models.

adjust_lag2_corrcoef1(gamma_1, gamma_2)

A simple adjustment of lag-2 temporal autocorrelation coefficient to ensure that the resulting AR(2) process is stationary when the parameters are estimated from the Yule-Walker equations.

adjust_lag2_corrcoef2(gamma_1, gamma_2)

A more advanced adjustment of lag-2 temporal autocorrelation coefficient to ensure that the resulting AR(2) process is stationary when the parameters are estimated from the Yule-Walker equations.

ar_acf(gamma[, n])

Compute theoretical autocorrelation function (ACF) from the AR(p) model with lag-l, l=1,2,...,p temporal autocorrelation coefficients.

estimate_ar_params_ols(x, p[, d, ...])

Estimate the parameters of an autoregressive AR(p) model

estimate_ar_params_ols_localized(x, p, ...)

Estimate the parameters of a localized AR(p) model

estimate_ar_params_yw(gamma[, d, ...])

Estimate the parameters of an AR(p) model

estimate_ar_params_yw_localized(gamma[, d])

Estimate the parameters of a localized AR(p) model

estimate_var_params_ols(x, p[, d, ...])

Estimate the parameters of a vector autoregressive VAR(p) model

estimate_var_params_ols_localized(x, p, ...)

Estimate the parameters of a vector autoregressive VAR(p) model

estimate_var_params_yw(gamma[, d, ...])

Estimate the parameters of a VAR(p) model

iterate_ar_model(x, phi[, eps])

Apply an AR(p) model

iterate_var_model(x, phi[, eps])

Apply a VAR(p) model

pysteps.timeseries.correlation#

Methods for computing spatial and temporal correlation of time series of two-dimensional fields.

temporal_autocorrelation(x[, d, domain, ...])

Compute lag-l temporal autocorrelation coefficients \(\gamma_l=\mbox{corr}(x(t),x(t-l))\), \(l=1,2,\dots,n-1\), from a time series \(x_1,x_2,\dots,x_n\).

temporal_autocorrelation_multivariate(x[, ...])

For a \(q\)-variate time series \(\mathbf{x}_1,\mathbf{x}_2,\dots,\mathbf{x}_n\), compute the lag-l correlation matrices \(\mathbf{\Gamma}_l\), where \(\Gamma_{l,i,j}=\gamma_{l,i,j}\) and \(\gamma_{l,i,j}=\mbox{corr}(x_i(t),x_j(t-l))\) for \(i,j=1,2,\dots,q\) and \(l=0,1,\dots,n-1\).