Teradata R Package Function Reference - CCM - Teradata R Package - Look here for syntax, methods and examples for the functions included in the Teradata R Package.

Description

The CCM (td_ccm_mle) function takes two or more time series as input and evaluates potential cause-effect relationships between them. Each time series column can be a single, long time series or a set of shorter subsequences that represent the same process. The function returns an effect size for each cause-effect pair.

Usage

  td_ccm_mle (
      data = NULL,
      sequence.id.column = NULL,
      time.column = NULL,
      cause.columns = NULL,
      effect.columns = NULL,
      library.size = 100,
      embedding.dimension = 2,
      time.step = 1,
      bootstrap.iterations = 100,
      predict.step = 1,
      self.predict = FALSE,
      seed = NULL,
      point.select.rule = "DistanceOnly",
      mode = "Single",
      data.sequence.column = NULL
  )

Arguments

`data`	Required Argument. Specifies the tbl_teradata containing the input data.
`sequence.id.column`	Required Argument. Specifies the column containing the sequence ids. A sequence is a sample of the time series.
`time.column`	Required Argument. Specifies the column containing the timestamps.
`cause.columns`	Required Argument. Specifies the columns to be evaluated as potential causes.
`effect.columns`	Required Argument. Specifies the columns to be evaluated as potential effects.
`library.size`	Optional Argument. 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. Default Value: 100
`embedding.dimension`	Optional Argument. 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. Default Value: 2
`time.step`	Optional Argument. Specifies 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. Default Value: 1
`bootstrap.iterations`	Optional Argument. Specifies 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. Default Value: 100
`predict.step`	Optional Argument. If the best embedding dimension is needed, this argument is used to specify the number of time steps into the future to make predictions from past observations. Default Value: 1
`self.predict`	Optional Argument. If this argument is set to TRUE, the td_ccm_mle function will attempt 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 td_ccm_mle algorithm to work effectively. Default Value: FALSE
`seed`	Optional Argument. Specifies the random seed used to initialize the algorithm.
`point.select.rule`	Optional Argument. Specifies the rules to select nearest points if the best embedding dimension is needed. Default Value: "DistanceOnly" Permitted Values: DistanceAndTime, DistanceOnly
`mode`	Optional Argument. Specifies the execution mode. CCM can be executed in single mode and distribute mode. Default Value: "Single" Permitted Values: Single, Distribute
`data.sequence.column`	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "data". The argument is used to ensure deterministic results for functions which produce results that vary from run to run.

Value

Function returns an object of class "td_ccm_mle" which is a named list containing Teradata tbl object. Named list member can be referenced directly with the "$" operator using name: result.

Examples

    # Get the current context/connection
    con <- td_get_context()$connection
    
    # Load example data.
    loadExampleData("ccmprepare_example", "ccmprepare_input")
    loadExampleData("ccm_example", "ccm_input", "ccm_input2")
    
    # Load the time series datasets
    ccm_input <- tbl(con, "ccm_input")
    ccm_input2 <- tbl(con, "ccm_input2")
    ccmprepare_input <- tbl(con, "ccmprepare_input")
    
    # Find causal-effect relationship between income, expenditure and investiment fields
    td_ccm_out <- td_ccm_mle(data = ccm_input,
                         sequence.id.column = "id",
                         time.column = "period",
                         cause.columns = c("income"),
                         effect.columns = c("expenditure","investment"),
                         seed = 0
                         )
    # Alternatively, the below example produces the same output as above
    # by making use of td_ccm_prepare_mle and then using its output object for td_ccm_mle
    td_ccm_prepare_out <- td_ccm_prepare_mle(data = ccmprepare_input,
                                     data.partition.column = "id"
                                    )
    td_ccm_out1 <- td_ccm_mle(data = td_ccm_prepare_out$result,
                     sequence.id.column = "id",
                     time.column = "period",
                     cause.columns = c("income"),
                     effect.columns = c("expenditure","investment"),
                     seed = 0
                     )

    
    # Another example to find the cause-effect relation on a sample market time series data
    td_ccm_out2 <- td_ccm_mle(data = ccm_input2,
                         sequence.id.column = "id",
                         time.column = "period",
                         cause.columns = c("marketindex","indexval"),
                         effect.columns = c("indexdate","indexchange"),
                         library.size = 10,
                         seed = 0
                         )