datarobot (version 2.18.6)

ComputeDatetimeTrendPlots: Compute datetime trend plots for datetime partitioned model.

Description

Compute datetime trend plots for datetime partitioned model. This includes Accuracy over Time, Forecast vs Actual, and Anomaly over Time plots.

Usage

ComputeDatetimeTrendPlots(
  model,
  backtest = 0,
  source = SourceType$Validation,
  forecastDistanceStart = NULL,
  forecastDistanceEnd = NULL
)

Value

An integer value that can be used as the jobId parameter in a subsequent call to WaitForJobToComplete.

Arguments

model

An S3 object of class dataRobotModel like that returned by the function GetModel, or each element of the list returned by the function ListModels.

backtest

integer or character. Optional. Compute plots for a specific backtest. Use the backtest index starting from zero. To compute plots for holdout, use DataSubset$Holdout.

source

character. Optional. The source of the data for the backtest/holdout. Must be one of SourceType.

forecastDistanceStart

integer. Optional. The start of forecast distance range (forecast window) to compute. If not specified, the first forecast distance for this project will be used. Only for time series supervised models.

forecastDistanceEnd

integer. Optional. The end of forecast distance range (forecast window) to compute. If not specified, the last forecast distance for this project will be used. Only for time series supervised models.

Details

• Forecast distance specifies the number of time steps between the predicted point and the origin point.

• For the multiseries models only first 1000 series in alphabetical order and an average plot for them will be computed.

• Maximum 100 forecast distances can be requested for calculation in time series supervised projects.

Examples

if (FALSE) {
projectId <- "59a5af20c80891534e3c2bde"
modelId <- "5996f820af07fc605e81ead4"
model <- GetModel(projectId, modelId)
jobId <- ComputeDatetimeTrendPlots(model)
WaitForJobToComplete(projectId, jobId) # optional step
}