Function CCM_boot [multispatialCCM v1.0]

keywords

ts

title

Run multispatial CCM algorithm on two time series

description

Runs the multispatial convergent cross mapping algorithm on two time series, A and B, to determine whether process A is a forcing process (i.e., causally affects) or process B. A and B do not need to be from single, long time series, but rather can be combinations of many (e.g. spatially-replicated) time series. See "Arguments" for details.

Function make_ccm_data [multispatialCCM v1.0]

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ts

title

Makes fake data for other functions

description

Builds a fake data set of two interacting processes, based on the model in the Sugihara et al. publication below, and based on a two-species discrete-time competition model. In the model, process A is causally affected by process B, but process B is not influenced by process A.

Function SSR_pred_boot [multispatialCCM v1.0]

keywords

ts

title

State space reconstruction function

description

Predict elements of A using B using state space reconstruction. If A=B, then the algorithm uses cross validation to assess the ability of historical portions of the A time series to predict future components of the time series. This function can be used to find the embedding dimension E that maximizes predictive ability.

Function SSR_check_signal [multispatialCCM v1.0]

keywords

ts

title

Test process for auto-predictability.

description

Predict elements of a process based historical observations of that process using cross-validation. Tests whether past observations are able to make good estimates of future elements of the time series.

Function ccmtest [multispatialCCM v1.0]

keywords

ts

title

Test for significant causal signal

description

Tests output from CCM_boot for significant causal signal. This function compares the 95% confidence intervals for esimated rho for the shortest and longest libraries calculated, and uses this to determine whether predictive power has significantly increased.

Function turnpoints [pastecs v1.3-18]

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ts

title

Analyze turning points (peaks or pits)

description

Determine the number and the position of extrema (turning points, either peaks or pits) in a regular time series. Calculate the quantity of information associated to the observations in this series, according to Kendall's information theory

Function garsim [gsarima v0.1-4]

keywords

ts

title

Simulate a Generalized Autoregressive Time Series

description

Simulate a time series using a general autoregressive model.

Function arrep [gsarima v0.1-4]

keywords

ts

title

Compute the Autoregressive Representation of a Sarima Model

description

Invert (invertible) SARIMA(p, d, q, P, D, Q) models to ar representation.

Function lagPlot [gamlss.util v4.3-4]

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ts

title

Lag plot for time series data

description

The function lagPlot() plots a time series variable against its lagged values or against the lagged values of an explanatory variable.

Function calc.trans [klaR v0.6-12]

keywords

ts

title

Calculation of transition probabilities

description

Function to estimate the probabilities of a time series to stay or change the state.