pysteps.timeseries#
Methods and models for time series analysis.
pysteps.timeseries.autoregression#
Methods related to autoregressive AR(p) models.
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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. |
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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. |
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Compute theoretical autocorrelation function (ACF) from the AR(p) model with lag-l, l=1,2,...,p temporal autocorrelation coefficients. |
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Estimate the parameters of an autoregressive AR(p) model |
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Estimate the parameters of a localized AR(p) model |
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Estimate the parameters of an AR(p) model |
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Estimate the parameters of a localized AR(p) model |
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Estimate the parameters of a vector autoregressive VAR(p) model |
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Estimate the parameters of a vector autoregressive VAR(p) model |
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Estimate the parameters of a VAR(p) model |
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Apply an AR(p) model |
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Apply a VAR(p) model |
pysteps.timeseries.correlation#
Methods for computing spatial and temporal correlation of time series of two-dimensional fields.
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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 \(x_1,x_2,\dots,x_n\). |
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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 \(l=0,1,\dots,n-1\). |