| Argument | Category | Description |
|---|---|---|
| InputTable | Required | Table containing the input data. |
| SequenceIdColumn | Required | Column containing the sequence ids. A sequence is a sample of the time series. |
| TimeColumn | Required | Column containing the timestamps. |
| CauseColumns | Required | Columns to be evaluated as potential causes. |
| EffectColumns | Required | Columns to be evaluated as potential effects. |
| LibrarySize | Optional | The CCM algorithm works by using “libraries” of randomly selected points along the potential effect time series to predict values of the cause time series. A causal relationship is said to exist if the correlation between the predicted values of the cause time series and the actual values increases as the size of the library increases. Each input value must be greater than 0. The default value is 100. |
| EmbeddingDimension | Optional | The embedding dimension is an estimate of the number of past values to use when predicting a given value of the time series. The input value must be greater than 0. The default value is 2. |
| TimeStep | Optional | The TimeStep parameter indicates the number of time steps between past values to use when predicting a given value of the time series. The input value must be greater than 0. The default value is 1. |
| BoostrapIterations | Optional | The number of bootstrap iterations used to predict. The bootstrap process is used to estimate the uncertainty associated with the predicted values. The input value must be greater than 0. The default value is 100. |
| PointsToTest | Optional | The number of data points to predict using the library. The default is to predict all valid points along all time series. |
| SelfPredict | Optional | If SelfPredict is has the value 'true', the function tries to predict each attribute using the attribute itself. If an attribute can predict its own time series well, the signal-to-noise ratio is too low for the CCM algorithm to work effectively. The default value is 'false'. |
| Seed | Optional | Specifies the random seed used to initialize the algorithm. |