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Datetime Partitioning

This page outlines the operations, endpoints, parameters, and example requests and responses for the Datetime Partitioning.

GET /api/v2/projects/{projectId}/datetimePartitioning/

Retrieve datetime partitioning configuration

The datetime partition object in the response describes the full partitioning parameters. Since it becomes available after the target has been fully specified and the project is ready for modeling, there are some additional fields available compared to the response from :http:post:/api/v2/projects/(projectId)/datetimePartitioning/.

The available training data corresponds to all the data available for training, while the primary training data corresponds to the data that can be used to train while ensuring that all backtests are available. If a model is trained with more data than is available in the primary training data, then all backtests may not have scores available.

All durations and datetimes will be specified in accordance with the :ref:timestamp and duration formatting rules<time_format>.

Code samples

# You can also use wget
curl -X GET http://10.97.110.93/api/v2/projects/{projectId}/datetimePartitioning/ \
  -H 'Accept: application/json' \
  -H 'Authorization: Bearer {access-token}'

Parameters

Name In Type Required Description
projectId path string true The project ID

Example responses

200 Response

{
  "aggregationType": "total",
  "autopilotDataSelectionMethod": "duration",
  "availableTrainingDuration": "string",
  "availableTrainingEndDate": "2019-08-24T14:15:22Z",
  "availableTrainingRowCount": 0,
  "availableTrainingStartDate": "2019-08-24T14:15:22Z",
  "backtests": [
    {
      "availableTrainingDuration": "string",
      "availableTrainingEndDate": "2019-08-24T14:15:22Z",
      "availableTrainingRowCount": 0,
      "availableTrainingStartDate": "2019-08-24T14:15:22Z",
      "gapDuration": "string",
      "gapEndDate": "2019-08-24T14:15:22Z",
      "gapRowCount": 0,
      "gapStartDate": "2019-08-24T14:15:22Z",
      "index": 0,
      "primaryTrainingDuration": "string",
      "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
      "primaryTrainingRowCount": 0,
      "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
      "totalRowCount": 0,
      "validationDuration": "string",
      "validationEndDate": "2019-08-24T14:15:22Z",
      "validationRowCount": 0,
      "validationStartDate": "2019-08-24T14:15:22Z"
    }
  ],
  "calendarId": "string",
  "calendarName": "string",
  "crossSeriesGroupByColumns": [
    "string"
  ],
  "dateFormat": "string",
  "datetimePartitionColumn": "string",
  "defaultToAPriori": true,
  "defaultToDoNotDerive": true,
  "defaultToKnownInAdvance": true,
  "differencingMethod": "auto",
  "disableHoldout": true,
  "featureDerivationWindowEnd": 0,
  "featureDerivationWindowStart": 0,
  "featureSettings": [
    {
      "aPriori": true,
      "doNotDerive": true,
      "featureName": "string",
      "knownInAdvance": true
    }
  ],
  "forecastWindowEnd": 0,
  "forecastWindowStart": 0,
  "gapDuration": "string",
  "gapEndDate": "2019-08-24T14:15:22Z",
  "gapRowCount": 0,
  "gapStartDate": "2019-08-24T14:15:22Z",
  "holdoutDuration": "string",
  "holdoutEndDate": "2019-08-24T14:15:22Z",
  "holdoutRowCount": 0,
  "holdoutStartDate": "2019-08-24T14:15:22Z",
  "isHoldoutModified": true,
  "modelSplits": 1,
  "multiseriesIdColumns": [
    "string"
  ],
  "numberOfBacktests": 1,
  "numberOfDoNotDeriveFeatures": 0,
  "numberOfKnownInAdvanceFeatures": 0,
  "partitioningWarnings": [
    {
      "backtestIndex": 0,
      "partition": "string",
      "warnings": [
        "string"
      ]
    }
  ],
  "periodicities": [
    {
      "timeSteps": 0,
      "timeUnit": "MILLISECOND"
    }
  ],
  "primaryTrainingDuration": "string",
  "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
  "primaryTrainingRowCount": 0,
  "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
  "projectId": "string",
  "totalRowCount": 0,
  "treatAsExponential": "auto",
  "useCrossSeriesFeatures": true,
  "useTimeSeries": true,
  "validationDuration": "string",
  "windowsBasisUnit": "MILLISECOND"
}

Responses

Status Meaning Description Schema
200 OK Generated datetime partitioning. FinalDatetimePartitioningResponse
422 Unprocessable Entity Partitioning has not been set. None

To perform this operation, you must be authenticated by means of one of the following methods:

BearerAuth

POST /api/v2/projects/{projectId}/datetimePartitioning/

Preview the fully specified datetime partitioning generated by the requested configuration.

Populates the full datetime partitioning that would be used if the same arguments were passed to :http:patch:/api/v2/projects/(projectId)/aim/ based on the requested configuration, generating defaults for all non-specified values, so that potential configurations can be tested prior to setting the target and applying a configuration.

useTimeSeries controls whether a time series project should be created or a normal project that uses datetime partitioning. See :ref:Time-Series Projects<time_series_overview> for more detail on the differences between time series projects and datetime partitioned projects. Time-series projects are only available to some users and use the additional settings of featureDerivationWindowStart and featureDerivationWindowEnd to establish feature derivation window and forecastWindowStart and forecastWindowEnd to establish a forecast window. The overview referenced above provides more information about using feature derivation and forecast windows.

When specifying a feature derivation window of a forecast window, the number of units it spans (end - start) must be an integer multiple of the timeStep of the datetimePartitionColumn.

All durations and datetimes should be specified in accordance with the :ref:timestamp and duration formatting rules<time_format>.

Code samples

# You can also use wget
curl -X POST http://10.97.110.93/api/v2/projects/{projectId}/datetimePartitioning/ \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json' \
  -H 'Authorization: Bearer {access-token}'

Body parameter

{
  "aggregationType": "total",
  "allowPartialHistoryTimeSeriesPredictions": false,
  "autopilotClusterList": [
    2
  ],
  "autopilotDataSelectionMethod": "duration",
  "backtests": [
    {
      "gapDuration": "string",
      "index": 0,
      "validationDuration": "string",
      "validationStartDate": "2019-08-24T14:15:22Z"
    }
  ],
  "calendarId": "string",
  "crossSeriesGroupByColumns": [
    "string"
  ],
  "datetimePartitionColumn": "string",
  "defaultToAPriori": true,
  "defaultToDoNotDerive": true,
  "defaultToKnownInAdvance": true,
  "differencingMethod": "auto",
  "disableHoldout": false,
  "featureDerivationWindowEnd": 0,
  "featureDerivationWindowStart": 0,
  "featureSettings": [
    {
      "aPriori": true,
      "doNotDerive": true,
      "featureName": "string",
      "knownInAdvance": true
    }
  ],
  "forecastWindowEnd": 0,
  "forecastWindowStart": 0,
  "gapDuration": "string",
  "holdoutDuration": "string",
  "holdoutEndDate": "2019-08-24T14:15:22Z",
  "holdoutStartDate": "2019-08-24T14:15:22Z",
  "isHoldoutModified": true,
  "modelSplits": 5,
  "multiseriesIdColumns": [
    "string"
  ],
  "numberOfBacktests": 1,
  "periodicities": [
    {
      "timeSteps": 0,
      "timeUnit": "MILLISECOND"
    }
  ],
  "treatAsExponential": "auto",
  "unsupervisedMode": false,
  "unsupervisedType": "anomaly",
  "useCrossSeriesFeatures": false,
  "useSupervisedFeatureReduction": true,
  "useTimeSeries": false,
  "validationDuration": "string",
  "windowsBasisUnit": "MILLISECOND"
}

Parameters

Name In Type Required Description
projectId path string true The project ID
body body DatetimePartitioningDataForOpenApi false none

Example responses

200 Response

{
  "aggregationType": "total",
  "autopilotDataSelectionMethod": "duration",
  "availableTrainingDuration": "string",
  "availableTrainingEndDate": "2019-08-24T14:15:22Z",
  "availableTrainingStartDate": "2019-08-24T14:15:22Z",
  "backtests": [
    {
      "availableTrainingDuration": "string",
      "availableTrainingEndDate": "2019-08-24T14:15:22Z",
      "availableTrainingStartDate": "2019-08-24T14:15:22Z",
      "gapDuration": "string",
      "gapEndDate": "2019-08-24T14:15:22Z",
      "gapStartDate": "2019-08-24T14:15:22Z",
      "index": 0,
      "primaryTrainingDuration": "string",
      "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
      "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
      "validationDuration": "string",
      "validationEndDate": "2019-08-24T14:15:22Z",
      "validationStartDate": "2019-08-24T14:15:22Z"
    }
  ],
  "calendarId": "string",
  "calendarName": "string",
  "crossSeriesGroupByColumns": [
    "string"
  ],
  "dateFormat": "string",
  "datetimePartitionColumn": "string",
  "defaultToAPriori": true,
  "defaultToDoNotDerive": true,
  "defaultToKnownInAdvance": true,
  "differencingMethod": "auto",
  "disableHoldout": true,
  "featureDerivationWindowEnd": 0,
  "featureDerivationWindowStart": 0,
  "featureSettings": [
    {
      "aPriori": true,
      "doNotDerive": true,
      "featureName": "string",
      "knownInAdvance": true
    }
  ],
  "forecastWindowEnd": 0,
  "forecastWindowStart": 0,
  "gapDuration": "string",
  "gapEndDate": "2019-08-24T14:15:22Z",
  "gapStartDate": "2019-08-24T14:15:22Z",
  "holdoutDuration": "string",
  "holdoutEndDate": "2019-08-24T14:15:22Z",
  "holdoutStartDate": "2019-08-24T14:15:22Z",
  "isHoldoutModified": true,
  "modelSplits": 1,
  "multiseriesIdColumns": [
    "string"
  ],
  "numberOfBacktests": 1,
  "numberOfDoNotDeriveFeatures": 0,
  "numberOfKnownInAdvanceFeatures": 0,
  "partitioningWarnings": [
    {
      "backtestIndex": 0,
      "partition": "string",
      "warnings": [
        "string"
      ]
    }
  ],
  "periodicities": [
    {
      "timeSteps": 0,
      "timeUnit": "MILLISECOND"
    }
  ],
  "primaryTrainingDuration": "string",
  "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
  "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
  "projectId": "string",
  "treatAsExponential": "auto",
  "useCrossSeriesFeatures": true,
  "useTimeSeries": true,
  "validationDuration": "string",
  "windowsBasisUnit": "MILLISECOND"
}

Responses

Status Meaning Description Schema
200 OK Generated datetime partitioning. DatetimePartitioningResponse
404 Not Found Requested feature was not found. None
422 Unprocessable Entity Partitioning generation failed. None

To perform this operation, you must be authenticated by means of one of the following methods:

BearerAuth

GET /api/v2/projects/{projectId}/optimizedDatetimePartitionings/

List all created optimized datetime partitioning configurations

Code samples

# You can also use wget
curl -X GET http://10.97.110.93/api/v2/projects/{projectId}/optimizedDatetimePartitionings/?limit=10 \
  -H 'Accept: application/json' \
  -H 'Authorization: Bearer {access-token}'

Parameters

Name In Type Required Description
offset query integer false This many results will be skipped.
limit query integer true At most this many results are returned.
projectId path string true The project ID

Example responses

200 Response

{
  "count": 0,
  "data": [
    {
      "datetimePartitionColumn": "string",
      "id": "string",
      "partitionData": {
        "aggregationType": "total",
        "autopilotDataSelectionMethod": "duration",
        "availableTrainingDuration": "string",
        "availableTrainingEndDate": "2019-08-24T14:15:22Z",
        "availableTrainingStartDate": "2019-08-24T14:15:22Z",
        "backtests": [
          {
            "availableTrainingDuration": "string",
            "availableTrainingEndDate": "2019-08-24T14:15:22Z",
            "availableTrainingStartDate": "2019-08-24T14:15:22Z",
            "gapDuration": "string",
            "gapEndDate": "2019-08-24T14:15:22Z",
            "gapStartDate": "2019-08-24T14:15:22Z",
            "index": 0,
            "primaryTrainingDuration": "string",
            "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
            "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
            "validationDuration": "string",
            "validationEndDate": "2019-08-24T14:15:22Z",
            "validationStartDate": "2019-08-24T14:15:22Z"
          }
        ],
        "calendarId": "string",
        "calendarName": "string",
        "crossSeriesGroupByColumns": [
          "string"
        ],
        "dateFormat": "string",
        "datetimePartitionColumn": "string",
        "defaultToAPriori": true,
        "defaultToDoNotDerive": true,
        "defaultToKnownInAdvance": true,
        "differencingMethod": "auto",
        "disableHoldout": true,
        "featureDerivationWindowEnd": 0,
        "featureDerivationWindowStart": 0,
        "featureSettings": [
          {
            "aPriori": true,
            "doNotDerive": true,
            "featureName": "string",
            "knownInAdvance": true
          }
        ],
        "forecastWindowEnd": 0,
        "forecastWindowStart": 0,
        "gapDuration": "string",
        "gapEndDate": "2019-08-24T14:15:22Z",
        "gapStartDate": "2019-08-24T14:15:22Z",
        "holdoutDuration": "string",
        "holdoutEndDate": "2019-08-24T14:15:22Z",
        "holdoutStartDate": "2019-08-24T14:15:22Z",
        "isHoldoutModified": true,
        "modelSplits": 1,
        "multiseriesIdColumns": [
          "string"
        ],
        "numberOfBacktests": 1,
        "numberOfDoNotDeriveFeatures": 0,
        "numberOfKnownInAdvanceFeatures": 0,
        "partitioningWarnings": [
          {
            "backtestIndex": 0,
            "partition": "string",
            "warnings": [
              "string"
            ]
          }
        ],
        "periodicities": [
          {
            "timeSteps": 0,
            "timeUnit": "MILLISECOND"
          }
        ],
        "primaryTrainingDuration": "string",
        "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
        "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
        "projectId": "string",
        "treatAsExponential": "auto",
        "useCrossSeriesFeatures": true,
        "useTimeSeries": true,
        "validationDuration": "string",
        "windowsBasisUnit": "MILLISECOND"
      },
      "target": "string"
    }
  ],
  "next": "http://example.com",
  "previous": "http://example.com"
}

Responses

Status Meaning Description Schema
200 OK List of optimized datetime partitionings for projectId OptimizedDatetimePartitioningListResponse

To perform this operation, you must be authenticated by means of one of the following methods:

BearerAuth

POST /api/v2/projects/{projectId}/optimizedDatetimePartitionings/

Create an optimized datetime partitioning configuration using the target.

Initializes a job to construct an optimized datetime partitioning using the date and target information to ensure that backtests sufficiently cover regions of interest in the target. This is an asynchronous job. The results of the asynchronous job (backtests and other parameters can be used in the synchronous version.

useTimeSeries controls whether a time series project should be created or a normal project that uses datetime partitioning. See :ref:Time-Series Projects<time_series_overview> for more detail on the differences between time series projects and datetime partitioned projects. Time-series projects are only available to some users and use the additional settings of featureDerivationWindowStart and featureDerivationWindowEnd to establish feature derivation window and forecastWindowStart and forecastWindowEnd to establish a forecast window. The overview referenced above provides more information about using feature derivation and forecast windows.

When specifying a feature derivation window of a forecast window, the number of units it spans (end - start) must be an integer multiple of the timeStep of the datetimePartitionColumn.

All durations and datetimes should be specified in accordance with the :ref:timestamp and duration formatting rules<time_format>.

Code samples

# You can also use wget
curl -X POST http://10.97.110.93/api/v2/projects/{projectId}/optimizedDatetimePartitionings/ \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json' \
  -H 'Authorization: Bearer {access-token}'

Body parameter

{
  "aggregationType": "total",
  "allowPartialHistoryTimeSeriesPredictions": false,
  "autopilotClusterList": [
    2
  ],
  "autopilotDataSelectionMethod": "duration",
  "backtests": [
    {
      "gapDuration": "string",
      "index": 0,
      "validationDuration": "string",
      "validationStartDate": "2019-08-24T14:15:22Z"
    }
  ],
  "calendarId": "string",
  "crossSeriesGroupByColumns": [
    "string"
  ],
  "datetimePartitionColumn": "string",
  "defaultToAPriori": true,
  "defaultToDoNotDerive": true,
  "defaultToKnownInAdvance": true,
  "differencingMethod": "auto",
  "disableHoldout": false,
  "featureDerivationWindowEnd": 0,
  "featureDerivationWindowStart": 0,
  "featureSettings": [
    {
      "aPriori": true,
      "doNotDerive": true,
      "featureName": "string",
      "knownInAdvance": true
    }
  ],
  "forecastWindowEnd": 0,
  "forecastWindowStart": 0,
  "gapDuration": "string",
  "holdoutDuration": "string",
  "holdoutEndDate": "2019-08-24T14:15:22Z",
  "holdoutStartDate": "2019-08-24T14:15:22Z",
  "isHoldoutModified": true,
  "modelSplits": 5,
  "multiseriesIdColumns": [
    "string"
  ],
  "numberOfBacktests": 1,
  "periodicities": [
    {
      "timeSteps": 0,
      "timeUnit": "MILLISECOND"
    }
  ],
  "target": "string",
  "treatAsExponential": "auto",
  "unsupervisedMode": false,
  "unsupervisedType": "anomaly",
  "useCrossSeriesFeatures": false,
  "useSupervisedFeatureReduction": true,
  "useTimeSeries": false,
  "validationDuration": "string",
  "windowsBasisUnit": "MILLISECOND"
}

Parameters

Name In Type Required Description
projectId path string true The project ID
body body OptimizedDatetimePartitioningData false none

Example responses

200 Response

{
  "aggregationType": "total",
  "autopilotDataSelectionMethod": "duration",
  "availableTrainingDuration": "string",
  "availableTrainingEndDate": "2019-08-24T14:15:22Z",
  "availableTrainingStartDate": "2019-08-24T14:15:22Z",
  "backtests": [
    {
      "availableTrainingDuration": "string",
      "availableTrainingEndDate": "2019-08-24T14:15:22Z",
      "availableTrainingStartDate": "2019-08-24T14:15:22Z",
      "gapDuration": "string",
      "gapEndDate": "2019-08-24T14:15:22Z",
      "gapStartDate": "2019-08-24T14:15:22Z",
      "index": 0,
      "primaryTrainingDuration": "string",
      "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
      "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
      "validationDuration": "string",
      "validationEndDate": "2019-08-24T14:15:22Z",
      "validationStartDate": "2019-08-24T14:15:22Z"
    }
  ],
  "calendarId": "string",
  "calendarName": "string",
  "crossSeriesGroupByColumns": [
    "string"
  ],
  "dateFormat": "string",
  "datetimePartitionColumn": "string",
  "defaultToAPriori": true,
  "defaultToDoNotDerive": true,
  "defaultToKnownInAdvance": true,
  "differencingMethod": "auto",
  "disableHoldout": true,
  "featureDerivationWindowEnd": 0,
  "featureDerivationWindowStart": 0,
  "featureSettings": [
    {
      "aPriori": true,
      "doNotDerive": true,
      "featureName": "string",
      "knownInAdvance": true
    }
  ],
  "forecastWindowEnd": 0,
  "forecastWindowStart": 0,
  "gapDuration": "string",
  "gapEndDate": "2019-08-24T14:15:22Z",
  "gapStartDate": "2019-08-24T14:15:22Z",
  "holdoutDuration": "string",
  "holdoutEndDate": "2019-08-24T14:15:22Z",
  "holdoutStartDate": "2019-08-24T14:15:22Z",
  "isHoldoutModified": true,
  "modelSplits": 1,
  "multiseriesIdColumns": [
    "string"
  ],
  "numberOfBacktests": 1,
  "numberOfDoNotDeriveFeatures": 0,
  "numberOfKnownInAdvanceFeatures": 0,
  "partitioningWarnings": [
    {
      "backtestIndex": 0,
      "partition": "string",
      "warnings": [
        "string"
      ]
    }
  ],
  "periodicities": [
    {
      "timeSteps": 0,
      "timeUnit": "MILLISECOND"
    }
  ],
  "primaryTrainingDuration": "string",
  "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
  "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
  "projectId": "string",
  "treatAsExponential": "auto",
  "useCrossSeriesFeatures": true,
  "useTimeSeries": true,
  "validationDuration": "string",
  "windowsBasisUnit": "MILLISECOND"
}

Responses

Status Meaning Description Schema
200 OK Generated datetime partitioning. DatetimePartitioningResponse

To perform this operation, you must be authenticated by means of one of the following methods:

BearerAuth

GET /api/v2/projects/{projectId}/optimizedDatetimePartitionings/{datetimePartitioningId}/

Retrieve optimized datetime partitioning configuration

The optimized datetime partition objects are structurally identical to the original datetime partition objects, however they are retrieved from a mongo database after creation as opposed to being calculated synchronously. The datetime partition object in the response describes the full partitioning parameters.

The available training data corresponds to all the data available for training, while the primary training data corresponds to the data that can be used to train while ensuring that all backtests are available. If a model is trained with more data than is available in the primary training data, then all backtests may not have scores available.

.. note:: All durations and datetimes should be specified in accordance with the :ref:timestamp and duration formatting rules <time_format>.

Code samples

# You can also use wget
curl -X GET http://10.97.110.93/api/v2/projects/{projectId}/optimizedDatetimePartitionings/{datetimePartitioningId}/ \
  -H 'Accept: application/json' \
  -H 'Authorization: Bearer {access-token}'

Parameters

Name In Type Required Description
projectId path string true The project ID
datetimePartitioningId path string true The ID of the datetime partitioning to retrieve.

Example responses

200 Response

{
  "aggregationType": "total",
  "autopilotDataSelectionMethod": "duration",
  "availableTrainingDuration": "string",
  "availableTrainingEndDate": "2019-08-24T14:15:22Z",
  "availableTrainingStartDate": "2019-08-24T14:15:22Z",
  "backtests": [
    {
      "availableTrainingDuration": "string",
      "availableTrainingEndDate": "2019-08-24T14:15:22Z",
      "availableTrainingStartDate": "2019-08-24T14:15:22Z",
      "gapDuration": "string",
      "gapEndDate": "2019-08-24T14:15:22Z",
      "gapStartDate": "2019-08-24T14:15:22Z",
      "index": 0,
      "primaryTrainingDuration": "string",
      "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
      "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
      "validationDuration": "string",
      "validationEndDate": "2019-08-24T14:15:22Z",
      "validationStartDate": "2019-08-24T14:15:22Z"
    }
  ],
  "calendarId": "string",
  "calendarName": "string",
  "crossSeriesGroupByColumns": [
    "string"
  ],
  "dateFormat": "string",
  "datetimePartitionColumn": "string",
  "defaultToAPriori": true,
  "defaultToDoNotDerive": true,
  "defaultToKnownInAdvance": true,
  "differencingMethod": "auto",
  "disableHoldout": true,
  "featureDerivationWindowEnd": 0,
  "featureDerivationWindowStart": 0,
  "featureSettings": [
    {
      "aPriori": true,
      "doNotDerive": true,
      "featureName": "string",
      "knownInAdvance": true
    }
  ],
  "forecastWindowEnd": 0,
  "forecastWindowStart": 0,
  "gapDuration": "string",
  "gapEndDate": "2019-08-24T14:15:22Z",
  "gapStartDate": "2019-08-24T14:15:22Z",
  "holdoutDuration": "string",
  "holdoutEndDate": "2019-08-24T14:15:22Z",
  "holdoutStartDate": "2019-08-24T14:15:22Z",
  "isHoldoutModified": true,
  "modelSplits": 1,
  "multiseriesIdColumns": [
    "string"
  ],
  "numberOfBacktests": 1,
  "numberOfDoNotDeriveFeatures": 0,
  "numberOfKnownInAdvanceFeatures": 0,
  "partitioningWarnings": [
    {
      "backtestIndex": 0,
      "partition": "string",
      "warnings": [
        "string"
      ]
    }
  ],
  "periodicities": [
    {
      "timeSteps": 0,
      "timeUnit": "MILLISECOND"
    }
  ],
  "primaryTrainingDuration": "string",
  "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
  "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
  "projectId": "string",
  "treatAsExponential": "auto",
  "useCrossSeriesFeatures": true,
  "useTimeSeries": true,
  "validationDuration": "string",
  "windowsBasisUnit": "MILLISECOND"
}

Responses

Status Meaning Description Schema
200 OK Optimized datetime partitioning configuration DatetimePartitioningResponse

To perform this operation, you must be authenticated by means of one of the following methods:

BearerAuth

GET /api/v2/projects/{projectId}/timeSeriesFeatureLog/

Retrieve the feature derivation log content and log length for a time series project as JSON.

The Time Series Feature Log provides details about the feature generation process for a time series project. It includes information about which features are generated and their priority,as well as the detected properties of the time series data such as whether the series is stationary, and periodicities detected.

This route is only supported for time series projects that have finished partitioning.

The feature derivation log will include information about:

  • Detected stationarity of the series, e.g., Series detected as non-stationary
  • Detected presence of multiplicative trend in the series, e.g., Multiplicative trend detected
  • Detected periodicities in the series, e.g., Detected periodicities: 7 day
  • Maximum number of feature to be generated, e.g., Maximum number of feature to be generated is 1440
  • Window sizes used in rolling statistics / lag extractors, e.g., The window sizes chosen to be: 2 months
  • Features that are specified as known-in-advance, e.g., Variables treated as apriori: holiday
  • Details about features generated as timeseries features, and their priority, e.g., Generating feature "date (actual)" from "date" (priority: 1)
  • Details about why certain variables are transformed in the input data, e.g., Generating variable "y (log)" from "y" because multiplicative trend is detected

Code samples

# You can also use wget
curl -X GET http://10.97.110.93/api/v2/projects/{projectId}/timeSeriesFeatureLog/ \
  -H 'Accept: application/json' \
  -H 'Authorization: Bearer {access-token}'

Parameters

Name In Type Required Description
offset query integer false Number of results to skip.
limit query integer false At most this many results are returned. The default may change without notice.
projectId path string true The project ID

Example responses

200 Response

{
  "count": 0,
  "featureLog": "string",
  "next": "http://example.com",
  "previous": "http://example.com",
  "totalLogLines": 0
}

Responses

Status Meaning Description Schema
200 OK none TimeSeriesFeatureLogListControllerResponse

To perform this operation, you must be authenticated by means of one of the following methods:

BearerAuth

GET /api/v2/projects/{projectId}/timeSeriesFeatureLog/file/

Retrieve a text file containing the time series project feature log.

The Time Series Feature Log provides details about the feature generation process for a time series project. It includes information about which features are generated and their priority,as well as the detected properties of the time series data such as whether the series is stationary, and periodicities detected.

This route is only supported for time series projects that have finished partitioning.

The feature derivation log will include information about:

  • Maximum number of feature to be generated, e.g., Limit on the maximum number of feature in this project is 500
  • Number of derived features tested during the feature generation process, e.g., Total number of derived features during the feature generation process is 571
  • Number of generated features removed during the feature reduction process e.g. Total number of features removed during the feature reduction process is 472
  • Number of remaining features after the combined feature generation and reduction process, e.g., The finalized number of features is 99
  • Detected stationarity of the series, e.g., Series detected as non-stationary
  • Detected presence of multiplicative trend in the series, e.g., Multiplicative trend detected
  • Detected periodicities in the series, e.g., Detected periodicities: 7 day
  • Window sizes used in rolling statistics / lag extractors, e.g., The window sizes chosen to be: 2 months (because the time step is 1 month and Feature Derivation Window is 2 months)
  • Features that are specified as known-in-advance, e.g., Variables treated as apriori: holiday
  • Details about why certain variables are transformed in the input data, e.g., Generating variable "y (log)" from "y" because multiplicative trend is detected
  • Details about features generated as time series features, and their priority, e.g., Generating feature "date (actual)" from "date" (priority: 1)

Code samples

# You can also use wget
curl -X GET http://10.97.110.93/api/v2/projects/{projectId}/timeSeriesFeatureLog/file/ \
  -H 'Authorization: Bearer {access-token}'

Parameters

Name In Type Required Description
projectId path string true The project ID

Responses

Status Meaning Description Schema
200 OK none None

Response Headers

Status Header Type Format Description
200 Content-Disposition string attachment;filename=<filename>.txt The suggested filename is dynamically generated
200 Content-Type string MIME type of the returned data

To perform this operation, you must be authenticated by means of one of the following methods:

BearerAuth

Schemas

BacktestNewMethodForOpenApi

{
  "index": 0,
  "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
  "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
  "validationEndDate": "2019-08-24T14:15:22Z",
  "validationStartDate": "2019-08-24T14:15:22Z"
}

Properties

Name Type Required Restrictions Description
index integer true none The index from zero of the backtest.
primaryTrainingEndDate string(date-time) true none A datetime string representing the end date of the primary training data for this backtest.
primaryTrainingStartDate string(date-time) true none A datetime string representing the start date of the primary training data for this backtest.
validationEndDate string(date-time) true none A datetime string representing the end date of the validation data for this backtest.
validationStartDate string(date-time) true none A datetime string representing the start date of the validation data for this backtest.

BacktestOldMethodForOpenApi

{
  "gapDuration": "string",
  "index": 0,
  "validationDuration": "string",
  "validationStartDate": "2019-08-24T14:15:22Z"
}

Properties

Name Type Required Restrictions Description
gapDuration string(duration) true none A duration string representing the duration of the gap between the training and the validation data for this backtest.
index integer true none The index from zero of the backtest.
validationDuration string(duration) true none A duration string representing the duration of the validation data for this backtest.
validationStartDate string(date-time) true none A datetime string representing the start date of the validation data for this backtest.

BacktestResponse

{
  "availableTrainingDuration": "string",
  "availableTrainingEndDate": "2019-08-24T14:15:22Z",
  "availableTrainingStartDate": "2019-08-24T14:15:22Z",
  "gapDuration": "string",
  "gapEndDate": "2019-08-24T14:15:22Z",
  "gapStartDate": "2019-08-24T14:15:22Z",
  "index": 0,
  "primaryTrainingDuration": "string",
  "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
  "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
  "validationDuration": "string",
  "validationEndDate": "2019-08-24T14:15:22Z",
  "validationStartDate": "2019-08-24T14:15:22Z"
}

Properties

Name Type Required Restrictions Description
availableTrainingDuration string(duration) true none The duration of the available training data for this backtest.
availableTrainingEndDate string(date-time) true none The end date of the available training data for this backtest.
availableTrainingStartDate string(date-time) true none The start date of the available training data for this backtest.
gapDuration string(duration) true none The duration of the gap between the training and the validation scoring data for this backtest.
gapEndDate string(date-time) true none The end date of the gap between the training and validation scoring data for this backtest.
gapStartDate string(date-time) true none The start date of the gap between the training and validation scoring data for this backtest.
index integer true none The index from zero of this backtest.
primaryTrainingDuration string(duration) true none The duration of the primary training data for this backtest.
primaryTrainingEndDate string(date-time) true none The end date of the primary training data for this backtest.
primaryTrainingStartDate string(date-time) true none The start date of the primary training data for this backtest.
validationDuration string true none The duration of the validation scoring data for this backtest.
validationEndDate string(date-time) true none The end date of the validation scoring data for this backtest.
validationStartDate string(date-time) true none The start date of the validation scoring data for this backtest.

DatetimePartitioningDataForOpenApi

{
  "aggregationType": "total",
  "allowPartialHistoryTimeSeriesPredictions": false,
  "autopilotClusterList": [
    2
  ],
  "autopilotDataSelectionMethod": "duration",
  "backtests": [
    {
      "gapDuration": "string",
      "index": 0,
      "validationDuration": "string",
      "validationStartDate": "2019-08-24T14:15:22Z"
    }
  ],
  "calendarId": "string",
  "crossSeriesGroupByColumns": [
    "string"
  ],
  "datetimePartitionColumn": "string",
  "defaultToAPriori": true,
  "defaultToDoNotDerive": true,
  "defaultToKnownInAdvance": true,
  "differencingMethod": "auto",
  "disableHoldout": false,
  "featureDerivationWindowEnd": 0,
  "featureDerivationWindowStart": 0,
  "featureSettings": [
    {
      "aPriori": true,
      "doNotDerive": true,
      "featureName": "string",
      "knownInAdvance": true
    }
  ],
  "forecastWindowEnd": 0,
  "forecastWindowStart": 0,
  "gapDuration": "string",
  "holdoutDuration": "string",
  "holdoutEndDate": "2019-08-24T14:15:22Z",
  "holdoutStartDate": "2019-08-24T14:15:22Z",
  "isHoldoutModified": true,
  "modelSplits": 5,
  "multiseriesIdColumns": [
    "string"
  ],
  "numberOfBacktests": 1,
  "periodicities": [
    {
      "timeSteps": 0,
      "timeUnit": "MILLISECOND"
    }
  ],
  "treatAsExponential": "auto",
  "unsupervisedMode": false,
  "unsupervisedType": "anomaly",
  "useCrossSeriesFeatures": false,
  "useSupervisedFeatureReduction": true,
  "useTimeSeries": false,
  "validationDuration": "string",
  "windowsBasisUnit": "MILLISECOND"
}

Properties

Name Type Required Restrictions Description
aggregationType string false none For multiseries projects only. The aggregation type to apply when creating cross-series features.
allowPartialHistoryTimeSeriesPredictions boolean false none Specifies whether the time series predictions can use partial historical data.
autopilotClusterList [integer]¦null false none A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to clustering.
autopilotDataSelectionMethod string false none The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.
backtests [oneOf] false none An array specifying individual backtests.

oneOf

Name Type Required Restrictions Description
» anonymous BacktestOldMethodForOpenApi false none Method 1 - pass validation and gap durations

xor

Name Type Required Restrictions Description
» anonymous BacktestNewMethodForOpenApi false none Method 2 - directly configure the start and end dates of each partition, including the training partition.

continued

Name Type Required Restrictions Description
calendarId string false none The ID of the calendar to be used in this project.
crossSeriesGroupByColumns [string] false none For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like "men's clothing", "sports equipment", etc.
datetimePartitionColumn string true none The date column that will be used as a datetime partition column.
defaultToAPriori boolean false none Renamed to defaultToKnownInAdvance.
defaultToDoNotDerive boolean false none For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the featureSettings parameter.
defaultToKnownInAdvance boolean false none For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the featureSettings parameter. See the :ref:Time Series Overview <time_series_overview> for more context.
differencingMethod string false none For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems simple and seasonal are not allowed. Parameter periodicities must be specified if seasonal is chosen. Defaults to auto.
disableHoldout boolean false none Whether to suppress allocating a holdout fold. If disableHoldout is set to true, holdoutStartDate and holdoutDuration must not be set.
featureDerivationWindowEnd integer false none For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.
featureDerivationWindowStart integer false none For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.
featureSettings [FeatureSetting] false none An array specifying per feature settings. Features can be left unspecified.
forecastWindowEnd integer false none For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.
forecastWindowStart integer false none For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.
gapDuration string(duration) false none The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D).
holdoutDuration string(duration) false none The duration of holdout scoring data. When specifying holdoutDuration, holdoutStartDate must also be specified. This attribute cannot be specified when disableHoldout is true.
holdoutEndDate string(date-time) false none The end date of holdout scoring data. When specifying holdoutEndDate, holdoutStartDate must also be specified. This attribute cannot be specified when disableHoldout is true.
holdoutStartDate string(date-time) false none The start date of holdout scoring data. When specifying holdoutStartDate, one of holdoutEndDate or holdoutDuration must also be specified. This attribute cannot be specified when disableHoldout is true.
isHoldoutModified boolean false none A boolean value indicating whether holdout settings (start/end dates) have been modified by user.
modelSplits integer false none Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data.
multiseriesIdColumns [string] false none May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:multiseries <multiseries> section of the time series documentation for more context.
numberOfBacktests integer false none The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.
periodicities [Periodicity] false none A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.
treatAsExponential string false none For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems always is not allowed. Defaults to auto.
unsupervisedMode boolean false none A boolean value indicating whether an unsupervised project should be created.
unsupervisedType string¦null false none The type of unsupervised project. Only valid when unsupervisedMode is true. If unsupervisedMode, defaults to anomaly.
useCrossSeriesFeatures boolean false none For multiseries projects only. Indicating whether to use cross-series features.
useSupervisedFeatureReduction boolean false none When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features.
useTimeSeries boolean false none A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.
validationDuration string(duration) false none The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular.
windowsBasisUnit string false none For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or ROW. If omitted, the default value is detected time unit.

Enumerated Values

Property Value
aggregationType total
aggregationType average
autopilotDataSelectionMethod duration
autopilotDataSelectionMethod rowCount
differencingMethod auto
differencingMethod none
differencingMethod simple
differencingMethod seasonal
treatAsExponential auto
treatAsExponential never
treatAsExponential always
unsupervisedType anomaly
unsupervisedType clustering
windowsBasisUnit MILLISECOND
windowsBasisUnit SECOND
windowsBasisUnit MINUTE
windowsBasisUnit HOUR
windowsBasisUnit DAY
windowsBasisUnit WEEK
windowsBasisUnit MONTH
windowsBasisUnit QUARTER
windowsBasisUnit YEAR
windowsBasisUnit ROW

DatetimePartitioningResponse

{
  "aggregationType": "total",
  "autopilotDataSelectionMethod": "duration",
  "availableTrainingDuration": "string",
  "availableTrainingEndDate": "2019-08-24T14:15:22Z",
  "availableTrainingStartDate": "2019-08-24T14:15:22Z",
  "backtests": [
    {
      "availableTrainingDuration": "string",
      "availableTrainingEndDate": "2019-08-24T14:15:22Z",
      "availableTrainingStartDate": "2019-08-24T14:15:22Z",
      "gapDuration": "string",
      "gapEndDate": "2019-08-24T14:15:22Z",
      "gapStartDate": "2019-08-24T14:15:22Z",
      "index": 0,
      "primaryTrainingDuration": "string",
      "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
      "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
      "validationDuration": "string",
      "validationEndDate": "2019-08-24T14:15:22Z",
      "validationStartDate": "2019-08-24T14:15:22Z"
    }
  ],
  "calendarId": "string",
  "calendarName": "string",
  "crossSeriesGroupByColumns": [
    "string"
  ],
  "dateFormat": "string",
  "datetimePartitionColumn": "string",
  "defaultToAPriori": true,
  "defaultToDoNotDerive": true,
  "defaultToKnownInAdvance": true,
  "differencingMethod": "auto",
  "disableHoldout": true,
  "featureDerivationWindowEnd": 0,
  "featureDerivationWindowStart": 0,
  "featureSettings": [
    {
      "aPriori": true,
      "doNotDerive": true,
      "featureName": "string",
      "knownInAdvance": true
    }
  ],
  "forecastWindowEnd": 0,
  "forecastWindowStart": 0,
  "gapDuration": "string",
  "gapEndDate": "2019-08-24T14:15:22Z",
  "gapStartDate": "2019-08-24T14:15:22Z",
  "holdoutDuration": "string",
  "holdoutEndDate": "2019-08-24T14:15:22Z",
  "holdoutStartDate": "2019-08-24T14:15:22Z",
  "isHoldoutModified": true,
  "modelSplits": 1,
  "multiseriesIdColumns": [
    "string"
  ],
  "numberOfBacktests": 1,
  "numberOfDoNotDeriveFeatures": 0,
  "numberOfKnownInAdvanceFeatures": 0,
  "partitioningWarnings": [
    {
      "backtestIndex": 0,
      "partition": "string",
      "warnings": [
        "string"
      ]
    }
  ],
  "periodicities": [
    {
      "timeSteps": 0,
      "timeUnit": "MILLISECOND"
    }
  ],
  "primaryTrainingDuration": "string",
  "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
  "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
  "projectId": "string",
  "treatAsExponential": "auto",
  "useCrossSeriesFeatures": true,
  "useTimeSeries": true,
  "validationDuration": "string",
  "windowsBasisUnit": "MILLISECOND"
}

Properties

Name Type Required Restrictions Description
aggregationType string¦null false none For multiseries projects only. The aggregation type to apply when creating cross-series features.
autopilotDataSelectionMethod string true none The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.
availableTrainingDuration string(duration) true none The duration of available training duration for scoring the holdout.
availableTrainingEndDate string(date-time) true none The end date of available training data for scoring the holdout.
availableTrainingStartDate string(date-time) true none The start date of available training data for scoring the holdout.
backtests [BacktestResponse] true none An array of the configured backtests.
calendarId string¦null false none The ID of the calendar to be used in this project.
calendarName string¦null false none The name of the calendar used in this project.
crossSeriesGroupByColumns [string]¦null false none For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like "men's clothing", "sports equipment", etc.
dateFormat string true none The date format of the partition column.
datetimePartitionColumn string true none The date column that will be used as a datetime partition column.
defaultToAPriori boolean true none Renamed to defaultToKnownInAdvance.
defaultToDoNotDerive boolean true none For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the featureSettings parameter.
defaultToKnownInAdvance boolean true none For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the featureSettings parameter. See the :ref:Time Series Overview <time_series_overview> for more context.
differencingMethod string¦null true none For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems simple and seasonal are not allowed. Parameter periodicities must be specified if seasonal is chosen. Defaults to auto.
disableHoldout boolean true none A boolean value indicating whether date partitioning skipped allocating a holdout fold.
featureDerivationWindowEnd integer¦null true none For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.
featureDerivationWindowStart integer¦null true none For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.
featureSettings [FeatureSetting] true none An array specifying per feature settings. Features can be left unspecified.
forecastWindowEnd integer¦null true none For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.
forecastWindowStart integer¦null true none For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.
gapDuration string(duration) true none The duration of the gap between the training and holdout scoring data.
gapEndDate string(date-time) true none The end date of the gap between the training and holdout scoring data.
gapStartDate string(date-time) true none The start date of the gap between the training and holdout scoring data.
holdoutDuration string(duration) true none The duration of the holdout scoring data.
holdoutEndDate string(date-time) true none The end date of holdout scoring data.
holdoutStartDate string(date-time) true none The start date of holdout scoring data.
isHoldoutModified boolean false none A boolean value indicating whether holdout settings (start/end dates) have been modified by user.
modelSplits integer false none Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data.
multiseriesIdColumns [string]¦null true none May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:multiseries <multiseries> section of the time series documentation for more context.
numberOfBacktests integer true none The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.
numberOfDoNotDeriveFeatures integer true none Number of features that are marked as "do not derive".
numberOfKnownInAdvanceFeatures integer true none Number of features that are marked as "known in advance".
partitioningWarnings [PartitioningWarning] true none An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.
periodicities [Periodicity]¦null true none A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.
primaryTrainingDuration string(duration) true none The duration of primary training duration for scoring the holdout.
primaryTrainingEndDate string(date-time) true none The end date of primary training data for scoring the holdout.
primaryTrainingStartDate string(date-time) true none The start date of primary training data for scoring the holdout.
projectId string true none The ID of the project.
treatAsExponential string¦null true none For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems always is not allowed. Defaults to auto.
useCrossSeriesFeatures boolean¦null false none For multiseries projects only. Indicating whether to use cross-series features.
useTimeSeries boolean true none A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.
validationDuration string(duration)¦null true none The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually.
windowsBasisUnit string¦null true none For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or ROW. If omitted, the default value is detected time unit.

Enumerated Values

Property Value
aggregationType total
aggregationType average
autopilotDataSelectionMethod duration
autopilotDataSelectionMethod rowCount
differencingMethod auto
differencingMethod none
differencingMethod simple
differencingMethod seasonal
treatAsExponential auto
treatAsExponential never
treatAsExponential always
windowsBasisUnit MILLISECOND
windowsBasisUnit SECOND
windowsBasisUnit MINUTE
windowsBasisUnit HOUR
windowsBasisUnit DAY
windowsBasisUnit WEEK
windowsBasisUnit MONTH
windowsBasisUnit QUARTER
windowsBasisUnit YEAR
windowsBasisUnit ROW

FeatureSetting

{
  "aPriori": true,
  "doNotDerive": true,
  "featureName": "string",
  "knownInAdvance": true
}

Properties

Name Type Required Restrictions Description
aPriori boolean false none Renamed to knownInAdvance.
doNotDerive boolean false none For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the defaultToDoNotDerive flag.
featureName string true none The name of the feature being specified.
knownInAdvance boolean false none For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the defaultToKnownInAdvance flag.

FinalBacktestResponse

{
  "availableTrainingDuration": "string",
  "availableTrainingEndDate": "2019-08-24T14:15:22Z",
  "availableTrainingRowCount": 0,
  "availableTrainingStartDate": "2019-08-24T14:15:22Z",
  "gapDuration": "string",
  "gapEndDate": "2019-08-24T14:15:22Z",
  "gapRowCount": 0,
  "gapStartDate": "2019-08-24T14:15:22Z",
  "index": 0,
  "primaryTrainingDuration": "string",
  "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
  "primaryTrainingRowCount": 0,
  "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
  "totalRowCount": 0,
  "validationDuration": "string",
  "validationEndDate": "2019-08-24T14:15:22Z",
  "validationRowCount": 0,
  "validationStartDate": "2019-08-24T14:15:22Z"
}

Properties

Name Type Required Restrictions Description
availableTrainingDuration string(duration) true none The duration of the available training data for this backtest.
availableTrainingEndDate string(date-time) true none The end date of the available training data for this backtest.
availableTrainingRowCount integer true none The number of rows in the available training data for this backtest.
availableTrainingStartDate string(date-time) true none The start date of the available training data for this backtest.
gapDuration string(duration) true none The duration of the gap between the training and the validation scoring data for this backtest.
gapEndDate string(date-time) true none The end date of the gap between the training and validation scoring data for this backtest.
gapRowCount integer true none The number of rows in the gap between the training and the validation scoring data for this backtest.
gapStartDate string(date-time) true none The start date of the gap between the training and validation scoring data for this backtest.
index integer true none The index from zero of this backtest.
primaryTrainingDuration string(duration) true none The duration of the primary training data for this backtest.
primaryTrainingEndDate string(date-time) true none The end date of the primary training data for this backtest.
primaryTrainingRowCount integer true none The number of rows in the primary training data for this backtest.
primaryTrainingStartDate string(date-time) true none The start date of the primary training data for this backtest.
totalRowCount integer true none The total number of rows in this backtest
validationDuration string true none The duration of the validation scoring data for this backtest.
validationEndDate string(date-time) true none The end date of the validation scoring data for this backtest.
validationRowCount integer true none The number of rows in the validation scoring data for this backtest.
validationStartDate string(date-time) true none The start date of the validation scoring data for this backtest.

FinalDatetimePartitioningResponse

{
  "aggregationType": "total",
  "autopilotDataSelectionMethod": "duration",
  "availableTrainingDuration": "string",
  "availableTrainingEndDate": "2019-08-24T14:15:22Z",
  "availableTrainingRowCount": 0,
  "availableTrainingStartDate": "2019-08-24T14:15:22Z",
  "backtests": [
    {
      "availableTrainingDuration": "string",
      "availableTrainingEndDate": "2019-08-24T14:15:22Z",
      "availableTrainingRowCount": 0,
      "availableTrainingStartDate": "2019-08-24T14:15:22Z",
      "gapDuration": "string",
      "gapEndDate": "2019-08-24T14:15:22Z",
      "gapRowCount": 0,
      "gapStartDate": "2019-08-24T14:15:22Z",
      "index": 0,
      "primaryTrainingDuration": "string",
      "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
      "primaryTrainingRowCount": 0,
      "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
      "totalRowCount": 0,
      "validationDuration": "string",
      "validationEndDate": "2019-08-24T14:15:22Z",
      "validationRowCount": 0,
      "validationStartDate": "2019-08-24T14:15:22Z"
    }
  ],
  "calendarId": "string",
  "calendarName": "string",
  "crossSeriesGroupByColumns": [
    "string"
  ],
  "dateFormat": "string",
  "datetimePartitionColumn": "string",
  "defaultToAPriori": true,
  "defaultToDoNotDerive": true,
  "defaultToKnownInAdvance": true,
  "differencingMethod": "auto",
  "disableHoldout": true,
  "featureDerivationWindowEnd": 0,
  "featureDerivationWindowStart": 0,
  "featureSettings": [
    {
      "aPriori": true,
      "doNotDerive": true,
      "featureName": "string",
      "knownInAdvance": true
    }
  ],
  "forecastWindowEnd": 0,
  "forecastWindowStart": 0,
  "gapDuration": "string",
  "gapEndDate": "2019-08-24T14:15:22Z",
  "gapRowCount": 0,
  "gapStartDate": "2019-08-24T14:15:22Z",
  "holdoutDuration": "string",
  "holdoutEndDate": "2019-08-24T14:15:22Z",
  "holdoutRowCount": 0,
  "holdoutStartDate": "2019-08-24T14:15:22Z",
  "isHoldoutModified": true,
  "modelSplits": 1,
  "multiseriesIdColumns": [
    "string"
  ],
  "numberOfBacktests": 1,
  "numberOfDoNotDeriveFeatures": 0,
  "numberOfKnownInAdvanceFeatures": 0,
  "partitioningWarnings": [
    {
      "backtestIndex": 0,
      "partition": "string",
      "warnings": [
        "string"
      ]
    }
  ],
  "periodicities": [
    {
      "timeSteps": 0,
      "timeUnit": "MILLISECOND"
    }
  ],
  "primaryTrainingDuration": "string",
  "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
  "primaryTrainingRowCount": 0,
  "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
  "projectId": "string",
  "totalRowCount": 0,
  "treatAsExponential": "auto",
  "useCrossSeriesFeatures": true,
  "useTimeSeries": true,
  "validationDuration": "string",
  "windowsBasisUnit": "MILLISECOND"
}

Properties

Name Type Required Restrictions Description
aggregationType string¦null true none For multiseries projects only. The aggregation type to apply when creating cross-series features.
autopilotDataSelectionMethod string true none The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.
availableTrainingDuration string(duration) true none The duration of available training duration for scoring the holdout.
availableTrainingEndDate string(date-time) true none The end date of available training data for scoring the holdout.
availableTrainingRowCount integer true none The number of rows in the available training data for scoring the holdout
availableTrainingStartDate string(date-time) true none The start date of available training data for scoring the holdout.
backtests [FinalBacktestResponse] true none An array of the configured backtests.
calendarId string¦null true none The ID of the calendar to be used in this project.
calendarName string¦null true none The name of the calendar used in this project.
crossSeriesGroupByColumns [string]¦null true none For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like "men's clothing", "sports equipment", etc.
dateFormat string true none The date format of the partition column.
datetimePartitionColumn string true none The date column that will be used as a datetime partition column.
defaultToAPriori boolean true none Renamed to defaultToKnownInAdvance.
defaultToDoNotDerive boolean true none For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the featureSettings parameter.
defaultToKnownInAdvance boolean true none For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the featureSettings parameter. See the :ref:Time Series Overview <time_series_overview> for more context.
differencingMethod string¦null true none For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems simple and seasonal are not allowed. Parameter periodicities must be specified if seasonal is chosen. Defaults to auto.
disableHoldout boolean true none A boolean value indicating whether date partitioning skipped allocating a holdout fold.
featureDerivationWindowEnd integer¦null true none For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.
featureDerivationWindowStart integer¦null true none For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.
featureSettings [FeatureSetting] true none An array specifying per feature settings. Features can be left unspecified.
forecastWindowEnd integer¦null true none For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.
forecastWindowStart integer¦null true none For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.
gapDuration string(duration) true none The duration of the gap between the training and holdout scoring data.
gapEndDate string(date-time) true none The end date of the gap between the training and holdout scoring data.
gapRowCount integer true none The number of rows in the gap between the training and holdout scoring data
gapStartDate string(date-time) true none The start date of the gap between the training and holdout scoring data.
holdoutDuration string(duration) true none The duration of the holdout scoring data.
holdoutEndDate string(date-time) true none The end date of holdout scoring data.
holdoutRowCount integer true none The number of rows in the holdout scoring data
holdoutStartDate string(date-time) true none The start date of holdout scoring data.
isHoldoutModified boolean false none A boolean value indicating whether holdout settings (start/end dates) have been modified by user.
modelSplits integer¦null true none Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data.
multiseriesIdColumns [string]¦null true none May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:multiseries <multiseries> section of the time series documentation for more context.
numberOfBacktests integer true none The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.
numberOfDoNotDeriveFeatures integer true none Number of features that are marked as "do not derive".
numberOfKnownInAdvanceFeatures integer true none Number of features that are marked as "known in advance".
partitioningWarnings [PartitioningWarning] true none An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.
periodicities [Periodicity]¦null true none A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.
primaryTrainingDuration string(duration) true none The duration of primary training duration for scoring the holdout.
primaryTrainingEndDate string(date-time) true none The end date of primary training data for scoring the holdout.
primaryTrainingRowCount integer true none The number of rows in the primary training data for scoring the holdout
primaryTrainingStartDate string(date-time) true none The start date of primary training data for scoring the holdout.
projectId string true none The ID of the project.
totalRowCount integer true none The total number of rows in the project dataset
treatAsExponential string¦null true none For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems always is not allowed. Defaults to auto.
useCrossSeriesFeatures boolean¦null true none For multiseries projects only. Indicating whether to use cross-series features.
useTimeSeries boolean true none A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.
validationDuration string(duration)¦null true none The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually.
windowsBasisUnit string¦null true none For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or ROW. If omitted, the default value is detected time unit.

Enumerated Values

Property Value
aggregationType total
aggregationType average
autopilotDataSelectionMethod duration
autopilotDataSelectionMethod rowCount
differencingMethod auto
differencingMethod none
differencingMethod simple
differencingMethod seasonal
treatAsExponential auto
treatAsExponential never
treatAsExponential always
windowsBasisUnit MILLISECOND
windowsBasisUnit SECOND
windowsBasisUnit MINUTE
windowsBasisUnit HOUR
windowsBasisUnit DAY
windowsBasisUnit WEEK
windowsBasisUnit MONTH
windowsBasisUnit QUARTER
windowsBasisUnit YEAR
windowsBasisUnit ROW

OptimizedDatetimePartitioningData

{
  "aggregationType": "total",
  "allowPartialHistoryTimeSeriesPredictions": false,
  "autopilotClusterList": [
    2
  ],
  "autopilotDataSelectionMethod": "duration",
  "backtests": [
    {
      "gapDuration": "string",
      "index": 0,
      "validationDuration": "string",
      "validationStartDate": "2019-08-24T14:15:22Z"
    }
  ],
  "calendarId": "string",
  "crossSeriesGroupByColumns": [
    "string"
  ],
  "datetimePartitionColumn": "string",
  "defaultToAPriori": true,
  "defaultToDoNotDerive": true,
  "defaultToKnownInAdvance": true,
  "differencingMethod": "auto",
  "disableHoldout": false,
  "featureDerivationWindowEnd": 0,
  "featureDerivationWindowStart": 0,
  "featureSettings": [
    {
      "aPriori": true,
      "doNotDerive": true,
      "featureName": "string",
      "knownInAdvance": true
    }
  ],
  "forecastWindowEnd": 0,
  "forecastWindowStart": 0,
  "gapDuration": "string",
  "holdoutDuration": "string",
  "holdoutEndDate": "2019-08-24T14:15:22Z",
  "holdoutStartDate": "2019-08-24T14:15:22Z",
  "isHoldoutModified": true,
  "modelSplits": 5,
  "multiseriesIdColumns": [
    "string"
  ],
  "numberOfBacktests": 1,
  "periodicities": [
    {
      "timeSteps": 0,
      "timeUnit": "MILLISECOND"
    }
  ],
  "target": "string",
  "treatAsExponential": "auto",
  "unsupervisedMode": false,
  "unsupervisedType": "anomaly",
  "useCrossSeriesFeatures": false,
  "useSupervisedFeatureReduction": true,
  "useTimeSeries": false,
  "validationDuration": "string",
  "windowsBasisUnit": "MILLISECOND"
}

Properties

Name Type Required Restrictions Description
aggregationType string false none For multiseries projects only. The aggregation type to apply when creating cross-series features.
allowPartialHistoryTimeSeriesPredictions boolean false none Specifies whether the time series predictions can use partial historical data.
autopilotClusterList [integer]¦null false none A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to clustering.
autopilotDataSelectionMethod string false none The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.
backtests [oneOf] false none An array specifying individual backtests.

oneOf

Name Type Required Restrictions Description
» anonymous BacktestOldMethodForOpenApi false none Method 1 - pass validation and gap durations

xor

Name Type Required Restrictions Description
» anonymous BacktestNewMethodForOpenApi false none Method 2 - directly configure the start and end dates of each partition, including the training partition.

continued

Name Type Required Restrictions Description
calendarId string false none The ID of the calendar to be used in this project.
crossSeriesGroupByColumns [string] false none For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like "men's clothing", "sports equipment", etc.
datetimePartitionColumn string true none The date column that will be used as a datetime partition column.
defaultToAPriori boolean false none Renamed to defaultToKnownInAdvance.
defaultToDoNotDerive boolean false none For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the featureSettings parameter.
defaultToKnownInAdvance boolean false none For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the featureSettings parameter. See the :ref:Time Series Overview <time_series_overview> for more context.
differencingMethod string false none For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems simple and seasonal are not allowed. Parameter periodicities must be specified if seasonal is chosen. Defaults to auto.
disableHoldout boolean false none Whether to suppress allocating a holdout fold. If disableHoldout is set to true, holdoutStartDate and holdoutDuration must not be set.
featureDerivationWindowEnd integer false none For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.
featureDerivationWindowStart integer false none For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.
featureSettings [FeatureSetting] false none An array specifying per feature settings. Features can be left unspecified.
forecastWindowEnd integer false none For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.
forecastWindowStart integer false none For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.
gapDuration string(duration) false none The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D).
holdoutDuration string(duration) false none The duration of holdout scoring data. When specifying holdoutDuration, holdoutStartDate must also be specified. This attribute cannot be specified when disableHoldout is true.
holdoutEndDate string(date-time) false none The end date of holdout scoring data. When specifying holdoutEndDate, holdoutStartDate must also be specified. This attribute cannot be specified when disableHoldout is true.
holdoutStartDate string(date-time) false none The start date of holdout scoring data. When specifying holdoutStartDate, one of holdoutEndDate or holdoutDuration must also be specified. This attribute cannot be specified when disableHoldout is true.
isHoldoutModified boolean false none A boolean value indicating whether holdout settings (start/end dates) have been modified by user.
modelSplits integer false none Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data.
multiseriesIdColumns [string] false none May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:multiseries <multiseries> section of the time series documentation for more context.
numberOfBacktests integer false none The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.
periodicities [Periodicity] false none A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.
target string false none Name of the target column.
treatAsExponential string false none For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems always is not allowed. Defaults to auto.
unsupervisedMode boolean false none A boolean value indicating whether an unsupervised project should be created.
unsupervisedType string¦null false none The type of unsupervised project. Only valid when unsupervisedMode is true. If unsupervisedMode, defaults to anomaly.
useCrossSeriesFeatures boolean false none For multiseries projects only. Indicating whether to use cross-series features.
useSupervisedFeatureReduction boolean false none When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features.
useTimeSeries boolean false none A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.
validationDuration string(duration) false none The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular.
windowsBasisUnit string false none For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or ROW. If omitted, the default value is detected time unit.

Enumerated Values

Property Value
aggregationType total
aggregationType average
autopilotDataSelectionMethod duration
autopilotDataSelectionMethod rowCount
differencingMethod auto
differencingMethod none
differencingMethod simple
differencingMethod seasonal
treatAsExponential auto
treatAsExponential never
treatAsExponential always
unsupervisedType anomaly
unsupervisedType clustering
windowsBasisUnit MILLISECOND
windowsBasisUnit SECOND
windowsBasisUnit MINUTE
windowsBasisUnit HOUR
windowsBasisUnit DAY
windowsBasisUnit WEEK
windowsBasisUnit MONTH
windowsBasisUnit QUARTER
windowsBasisUnit YEAR
windowsBasisUnit ROW

OptimizedDatetimePartitioningList

{
  "datetimePartitionColumn": "string",
  "id": "string",
  "partitionData": {
    "aggregationType": "total",
    "autopilotDataSelectionMethod": "duration",
    "availableTrainingDuration": "string",
    "availableTrainingEndDate": "2019-08-24T14:15:22Z",
    "availableTrainingStartDate": "2019-08-24T14:15:22Z",
    "backtests": [
      {
        "availableTrainingDuration": "string",
        "availableTrainingEndDate": "2019-08-24T14:15:22Z",
        "availableTrainingStartDate": "2019-08-24T14:15:22Z",
        "gapDuration": "string",
        "gapEndDate": "2019-08-24T14:15:22Z",
        "gapStartDate": "2019-08-24T14:15:22Z",
        "index": 0,
        "primaryTrainingDuration": "string",
        "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
        "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
        "validationDuration": "string",
        "validationEndDate": "2019-08-24T14:15:22Z",
        "validationStartDate": "2019-08-24T14:15:22Z"
      }
    ],
    "calendarId": "string",
    "calendarName": "string",
    "crossSeriesGroupByColumns": [
      "string"
    ],
    "dateFormat": "string",
    "datetimePartitionColumn": "string",
    "defaultToAPriori": true,
    "defaultToDoNotDerive": true,
    "defaultToKnownInAdvance": true,
    "differencingMethod": "auto",
    "disableHoldout": true,
    "featureDerivationWindowEnd": 0,
    "featureDerivationWindowStart": 0,
    "featureSettings": [
      {
        "aPriori": true,
        "doNotDerive": true,
        "featureName": "string",
        "knownInAdvance": true
      }
    ],
    "forecastWindowEnd": 0,
    "forecastWindowStart": 0,
    "gapDuration": "string",
    "gapEndDate": "2019-08-24T14:15:22Z",
    "gapStartDate": "2019-08-24T14:15:22Z",
    "holdoutDuration": "string",
    "holdoutEndDate": "2019-08-24T14:15:22Z",
    "holdoutStartDate": "2019-08-24T14:15:22Z",
    "isHoldoutModified": true,
    "modelSplits": 1,
    "multiseriesIdColumns": [
      "string"
    ],
    "numberOfBacktests": 1,
    "numberOfDoNotDeriveFeatures": 0,
    "numberOfKnownInAdvanceFeatures": 0,
    "partitioningWarnings": [
      {
        "backtestIndex": 0,
        "partition": "string",
        "warnings": [
          "string"
        ]
      }
    ],
    "periodicities": [
      {
        "timeSteps": 0,
        "timeUnit": "MILLISECOND"
      }
    ],
    "primaryTrainingDuration": "string",
    "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
    "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
    "projectId": "string",
    "treatAsExponential": "auto",
    "useCrossSeriesFeatures": true,
    "useTimeSeries": true,
    "validationDuration": "string",
    "windowsBasisUnit": "MILLISECOND"
  },
  "target": "string"
}

Properties

Name Type Required Restrictions Description
datetimePartitionColumn string true none The date column that will be used as a datetime partition column.
id string true none The ID of the datetime partitioning.
partitionData DatetimePartitioningResponse true none The datetime partitioning configuration.
target string true none The name of the target column.

OptimizedDatetimePartitioningListResponse

{
  "count": 0,
  "data": [
    {
      "datetimePartitionColumn": "string",
      "id": "string",
      "partitionData": {
        "aggregationType": "total",
        "autopilotDataSelectionMethod": "duration",
        "availableTrainingDuration": "string",
        "availableTrainingEndDate": "2019-08-24T14:15:22Z",
        "availableTrainingStartDate": "2019-08-24T14:15:22Z",
        "backtests": [
          {
            "availableTrainingDuration": "string",
            "availableTrainingEndDate": "2019-08-24T14:15:22Z",
            "availableTrainingStartDate": "2019-08-24T14:15:22Z",
            "gapDuration": "string",
            "gapEndDate": "2019-08-24T14:15:22Z",
            "gapStartDate": "2019-08-24T14:15:22Z",
            "index": 0,
            "primaryTrainingDuration": "string",
            "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
            "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
            "validationDuration": "string",
            "validationEndDate": "2019-08-24T14:15:22Z",
            "validationStartDate": "2019-08-24T14:15:22Z"
          }
        ],
        "calendarId": "string",
        "calendarName": "string",
        "crossSeriesGroupByColumns": [
          "string"
        ],
        "dateFormat": "string",
        "datetimePartitionColumn": "string",
        "defaultToAPriori": true,
        "defaultToDoNotDerive": true,
        "defaultToKnownInAdvance": true,
        "differencingMethod": "auto",
        "disableHoldout": true,
        "featureDerivationWindowEnd": 0,
        "featureDerivationWindowStart": 0,
        "featureSettings": [
          {
            "aPriori": true,
            "doNotDerive": true,
            "featureName": "string",
            "knownInAdvance": true
          }
        ],
        "forecastWindowEnd": 0,
        "forecastWindowStart": 0,
        "gapDuration": "string",
        "gapEndDate": "2019-08-24T14:15:22Z",
        "gapStartDate": "2019-08-24T14:15:22Z",
        "holdoutDuration": "string",
        "holdoutEndDate": "2019-08-24T14:15:22Z",
        "holdoutStartDate": "2019-08-24T14:15:22Z",
        "isHoldoutModified": true,
        "modelSplits": 1,
        "multiseriesIdColumns": [
          "string"
        ],
        "numberOfBacktests": 1,
        "numberOfDoNotDeriveFeatures": 0,
        "numberOfKnownInAdvanceFeatures": 0,
        "partitioningWarnings": [
          {
            "backtestIndex": 0,
            "partition": "string",
            "warnings": [
              "string"
            ]
          }
        ],
        "periodicities": [
          {
            "timeSteps": 0,
            "timeUnit": "MILLISECOND"
          }
        ],
        "primaryTrainingDuration": "string",
        "primaryTrainingEndDate": "2019-08-24T14:15:22Z",
        "primaryTrainingStartDate": "2019-08-24T14:15:22Z",
        "projectId": "string",
        "treatAsExponential": "auto",
        "useCrossSeriesFeatures": true,
        "useTimeSeries": true,
        "validationDuration": "string",
        "windowsBasisUnit": "MILLISECOND"
      },
      "target": "string"
    }
  ],
  "next": "http://example.com",
  "previous": "http://example.com"
}

Properties

Name Type Required Restrictions Description
count integer false none Number of items returned on this page.
data [OptimizedDatetimePartitioningList] true none A list of datetime partitionings returned in order of creation.
next string(uri)¦null true none URL pointing to the next page (if null, there is no next page).
previous string(uri)¦null true none URL pointing to the previous page (if null, there is no previous page).

PartitioningWarning

{
  "backtestIndex": 0,
  "partition": "string",
  "warnings": [
    "string"
  ]
}

Properties

Name Type Required Restrictions Description
backtestIndex integer¦null true none Backtest index. Null if the warning does not correspond to a single backtest.
partition string true none Partition name.
warnings [string] true none A list of strings representing warnings for the specified partition

Periodicity

{
  "timeSteps": 0,
  "timeUnit": "MILLISECOND"
}

Properties

Name Type Required Restrictions Description
timeSteps integer true none The number of time steps.
timeUnit string true none The time unit or ROW if windowsBasisUnit is ROW

Enumerated Values

Property Value
timeUnit MILLISECOND
timeUnit SECOND
timeUnit MINUTE
timeUnit HOUR
timeUnit DAY
timeUnit WEEK
timeUnit MONTH
timeUnit QUARTER
timeUnit YEAR
timeUnit ROW

TimeSeriesFeatureLogListControllerResponse

{
  "count": 0,
  "featureLog": "string",
  "next": "http://example.com",
  "previous": "http://example.com",
  "totalLogLines": 0
}

Properties

Name Type Required Restrictions Description
count integer true none The number of items returned on this page.
featureLog string true none The content of the feature log.
next string(uri)¦null true none URL pointing to the next page (if null, there is no next page).
previous string(uri)¦null true none URL pointing to the previous page (if null, there is no previous page).
totalLogLines integer true none The total number of lines in feature derivation log.

Updated June 28, 2022
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