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 \(l=0,1,\dots,n-1\). |