swagger: '2.0'
info:
  title: Microsoft Azure Anomaly Detector
  version: v1.1
  description: >-
    The Anomaly Detector API detects anomalies automatically in time series
    data.

    It supports both a stateless detection mode and a

    stateful detection mode. In stateless mode, there are three functionalities.
    Entire

    Detect is for detecting the whole series, with the model trained by the time
    series.

    Last Detect is for detecting the last point, with the model trained by
    points before.

    ChangePoint Detect is for detecting trend changes in the time series. In
    stateful

    mode, the user can store time series. The stored time series will be used
    for

    detection anomalies. In this mode, the user can still use the preceding
    three

    functionalities by only giving a time range without preparing time series on
    the

    client side. Besides the preceding three functionalities, the stateful model

    provides group-based detection and labeling services. By using the labeling

    service, the user can provide labels for each detection result. These labels
    will be

    used for retuning or regenerating detection models. Inconsistency detection
    is

    a kind of group-based detection that finds inconsistencies in

    a set of time series. By using the anomaly detector service, business
    customers can

    discover incidents and establish a logic flow for root cause analysis.
  x-typespec-generated:
    - emitter: '@azure-tools/typespec-autorest'
schemes:
  - https
x-ms-parameterized-host:
  hostTemplate: '{Endpoint}/anomalydetector/{ApiVersion}'
  useSchemePrefix: false
  parameters:
    - name: Endpoint
      in: path
      description: >-
        Supported Azure Cognitive Services endpoints (protocol and host name,
        such as

        https://westus2.api.cognitive.microsoft.com).
      required: true
      type: string
    - name: ApiVersion
      in: path
      description: Api Version
      required: true
      type: string
      enum:
        - v1.1
      x-ms-enum:
        name: APIVersion
        modelAsString: true
        values:
          - name: v1_1
            value: v1.1
produces:
  - application/json
consumes:
  - application/json
security:
  - AnomalyDetectorApiKeyAuth: []
securityDefinitions:
  AnomalyDetectorApiKeyAuth:
    type: apiKey
    description: The secret key for your Azure Cognitive Services subscription.
    name: Ocp-Apim-Subscription-Key
    in: header
tags:
  - name: Multivariate
  - name: Timeseries
paths:
  /multivariate/detect-batch/{resultId}:
    get:
      operationId: microsoftAzureMultivariateGetmultivariatebatchdetectionresult
      summary: Microsoft Azure Get Multivariate Anomaly Detection Result
      description: >-
        For asynchronous inference, get a multivariate anomaly detection result
        based on the<br>resultId value that the BatchDetectAnomaly API returns.
      parameters:
        - name: resultId
          in: path
          description: ID of a batch detection result.
          required: true
          type: string
          format: uuid
      responses:
        '200':
          description: The request has succeeded.
          schema:
            $ref: '#/definitions/Multivariate.MultivariateDetectionResult'
        default:
          description: An unexpected error response.
          schema:
            $ref: '#/definitions/Multivariate.ResponseError'
          headers:
            x-ms-error-code:
              type: string
              description: Error code.
      x-ms-examples:
        Get multivariate batch detection result:
          $ref: ./examples/GetResult.json
      tags:
        - Multivariate
  /multivariate/models:
    get:
      operationId: microsoftAzureMultivariateListmultivariatemodels
      summary: Microsoft Azure List Multivariate Models
      description: List models of a resource.
      parameters:
        - $ref: '#/parameters/Azure.Core.SkipQueryParameter'
        - $ref: '#/parameters/Azure.Core.TopQueryParameter'
      responses:
        '200':
          description: The request has succeeded.
          schema:
            $ref: '#/definitions/Multivariate.ModelList'
        default:
          description: An unexpected error response.
          schema:
            $ref: '#/definitions/Multivariate.ResponseError'
          headers:
            x-ms-error-code:
              type: string
              description: Error code.
      x-ms-examples:
        List multivariate models:
          $ref: ./examples/ListModel.json
      x-ms-pageable:
        nextLinkName: nextLink
      tags:
        - Multivariate
    post:
      operationId: microsoftAzureMultivariateTrainmultivariatemodel
      summary: Microsoft Azure Train A Multivariate Anomaly Detection Model
      description: >-
        Create and train a multivariate anomaly detection model. The request
        must<br>include a source parameter to indicate an Azure Blob<br>Storage
        URI that's accessible to the service. There are two types of data input.
        The Blob Storage URI can point to an Azure Blob<br>Storage folder that
        contains multiple CSV files, where each CSV file has<br>two columns,
        time stamp and variable. Or the Blob Storage URI can point to a single
        blob that contains a CSV file that has all the variables and a<br>time
        stamp column.<br>The model object will be created and returned in the
        response, but the<br>training process happens asynchronously. To check
        the training status, call<br>GetMultivariateModel with the modelId value
        and check the status field in the<br>modelInfo object.
      parameters:
        - name: modelInfo
          in: body
          description: Model information.
          required: true
          schema:
            $ref: '#/definitions/Multivariate.ModelInfo'
      responses:
        '201':
          description: >-
            The request has succeeded and a new resource has been created as a
            result.
          schema:
            $ref: '#/definitions/Multivariate.AnomalyDetectionModel'
          headers:
            location:
              type: string
              description: Location and ID of the model.
        default:
          description: An unexpected error response.
          schema:
            $ref: '#/definitions/Multivariate.ResponseError'
          headers:
            x-ms-error-code:
              type: string
              description: Error code.
      x-ms-examples:
        Create and train multivariate model:
          $ref: ./examples/TrainModel.json
      tags:
        - Multivariate
  /multivariate/models/{modelId}:
    get:
      operationId: microsoftAzureMultivariateGetmultivariatemodel
      summary: Microsoft Azure Get Multivariate Model
      description: >-
        Get detailed information about the multivariate model, including the
        training status<br>and variables used in the model.
      parameters:
        - name: modelId
          in: path
          description: Model identifier.
          required: true
          type: string
      responses:
        '200':
          description: The request has succeeded.
          schema:
            $ref: '#/definitions/Multivariate.AnomalyDetectionModel'
        default:
          description: An unexpected error response.
          schema:
            $ref: '#/definitions/Multivariate.ResponseError'
          headers:
            x-ms-error-code:
              type: string
              description: Error code.
      x-ms-examples:
        Get a multivariate model:
          $ref: ./examples/GetModel.json
      tags:
        - Multivariate
    delete:
      operationId: microsoftAzureMultivariateDeletemultivariatemodel
      summary: Microsoft Azure Delete Multivariate Model
      description: Delete an existing multivariate model according to the modelId value.
      parameters:
        - name: modelId
          in: path
          description: Model identifier.
          required: true
          type: string
      responses:
        '204':
          description: >-
            There is no content to send for this request, but the headers may be
            useful. 
        default:
          description: An unexpected error response.
          schema:
            $ref: '#/definitions/Multivariate.ResponseError'
          headers:
            x-ms-error-code:
              type: string
              description: Error code.
      x-ms-examples:
        Delete multivariate model:
          $ref: ./examples/DeleteModel.json
      tags:
        - Multivariate
  /multivariate/models/{modelId}:detect-batch:
    post:
      operationId: microsoftAzureMultivariateDetectmultivariatebatchanomaly
      summary: Microsoft Azure Detect Multivariate Anomaly
      description: >-
        Submit a multivariate anomaly detection task with the modelId value of a
        trained model<br>and inference data. The input schema should be the same
        with the training<br>request. The request will finish asynchronously and
        return a resultId value to<br>query the detection result. The request
        should be a source link to indicate an<br>externally accessible Azure
        Storage URI that either points to an Azure Blob<br>Storage folder or
        points to a CSV file in Azure Blob Storage.
      parameters:
        - name: modelId
          in: path
          description: Model identifier.
          required: true
          type: string
        - name: options
          in: body
          description: Request of multivariate anomaly detection.
          required: true
          schema:
            $ref: '#/definitions/Multivariate.MultivariateBatchDetectionOptions'
      responses:
        '202':
          description: >-
            The request has been accepted for processing, but processing has not
            yet completed.
          schema:
            $ref: '#/definitions/Multivariate.MultivariateDetectionResult'
          headers:
            Operation-Id:
              type: string
              description: ID of the detection result.
            Operation-Location:
              type: string
              description: Location of the detection result.
        default:
          description: An unexpected error response.
          schema:
            $ref: '#/definitions/Multivariate.ResponseError'
          headers:
            x-ms-error-code:
              type: string
              description: Error code.
      x-ms-examples:
        Detect multivariate batch anomaly:
          $ref: ./examples/DetectAnomaly.json
      tags:
        - Multivariate
  /multivariate/models/{modelId}:detect-last:
    post:
      operationId: microsoftAzureMultivariateDetectmultivariatelastanomaly
      summary: Microsoft Azure Detect Anomalies In The Last Point Of The Request Body
      description: >-
        Submit a multivariate anomaly detection task with the modelId value of a
        trained model<br>and inference data. The inference data should be put
        into the request body in<br>JSON format. The request will finish
        synchronously and return the detection<br>immediately in the response
        body.
      parameters:
        - name: modelId
          in: path
          description: Model identifier.
          required: true
          type: string
        - name: options
          in: body
          description: Request of the last detection.
          required: true
          schema:
            $ref: '#/definitions/Multivariate.MultivariateLastDetectionOptions'
      responses:
        '200':
          description: The request has succeeded.
          schema:
            $ref: '#/definitions/Multivariate.MultivariateLastDetectionResult'
        default:
          description: An unexpected error response.
          schema:
            $ref: '#/definitions/Multivariate.ResponseError'
          headers:
            x-ms-error-code:
              type: string
              description: Error code.
      x-ms-examples:
        Detect multivariate last anomaly:
          $ref: ./examples/LastDetectAnomaly.json
      tags:
        - Multivariate
  /timeseries/changepoint/detect:
    post:
      operationId: microsoftAzureUnivariateDetectunivariatechangepoint
      summary: Microsoft Azure Detect Change Point For The Entire Series
      description: Evaluate the change point score of every series point.
      parameters:
        - name: options
          in: body
          description: Method of univariate anomaly detection.
          required: true
          schema:
            $ref: '#/definitions/Univariate.UnivariateChangePointDetectionOptions'
      responses:
        '200':
          description: The request has succeeded.
          schema:
            $ref: '#/definitions/Univariate.UnivariateChangePointDetectionResult'
        default:
          description: An unexpected error response.
          schema:
            $ref: '#/definitions/Univariate.AnomalyDetectorError'
          headers:
            x-ms-error-code:
              type: string
              description: Error code.
      x-ms-examples:
        Univariate detection of a change point:
          $ref: ./examples/ChangePointDetect.json
      tags:
        - Timeseries
  /timeseries/entire/detect:
    post:
      operationId: microsoftAzureUnivariateDetectunivariateentireseries
      summary: 'Microsoft Azure Detect Anomalies For The Entire Series In Batch'
      description: >-
        This operation generates a model with an entire series. Each point is
        detected<br>with the same model. With this method, points before and
        after a certain point<br>are used to determine whether it's an anomaly.
        The entire detection can give the<br>user an overall status of the time
        series.
      parameters:
        - name: options
          in: body
          description: Method of univariate anomaly detection.
          required: true
          schema:
            $ref: '#/definitions/Univariate.UnivariateDetectionOptions'
      responses:
        '200':
          description: The request has succeeded.
          schema:
            $ref: '#/definitions/Univariate.UnivariateEntireDetectionResult'
        default:
          description: An unexpected error response.
          schema:
            $ref: '#/definitions/Univariate.AnomalyDetectorError'
          headers:
            x-ms-error-code:
              type: string
              description: Error code.
      x-ms-examples:
        Univariate detect entire series:
          $ref: ./examples/EntireDetect.json
      tags:
        - Timeseries
  /timeseries/last/detect:
    post:
      operationId: microsoftAzureUnivariateDetectunivariatelastpoint
      summary: 'Microsoft Azure Detect Anomaly Status Of The Latest Point In Time Series'
      description: >-
        This operation generates a model by using the points that you sent in to
        the API<br>and based on all data to determine whether the last point is
        anomalous.
      parameters:
        - name: options
          in: body
          description: Method of univariate anomaly detection.
          required: true
          schema:
            $ref: '#/definitions/Univariate.UnivariateDetectionOptions'
      responses:
        '200':
          description: The request has succeeded.
          schema:
            $ref: '#/definitions/Univariate.UnivariateLastDetectionResult'
        default:
          description: An unexpected error response.
          schema:
            $ref: '#/definitions/Univariate.AnomalyDetectorError'
          headers:
            x-ms-error-code:
              type: string
              description: Error code.
      x-ms-examples:
        Detect univariate last point:
          $ref: ./examples/LastDetect.json
      tags:
        - Timeseries
definitions:
  Azure.Core.uuid:
    type: string
    format: uuid
    description: Universally Unique Identifier
  Multivariate.AlignMode:
    type: string
    enum:
      - Inner
      - Outer
    x-ms-enum:
      name: AlignMode
      modelAsString: true
  Multivariate.AlignPolicy:
    type: object
    description: Manner of aligning multiple variables.
    properties:
      alignMode:
        $ref: '#/definitions/Multivariate.AlignMode'
        description: |-
          Field that indicates how to align different variables to the same
          time range.
      fillNAMethod:
        $ref: '#/definitions/Multivariate.FillNAMethod'
        description: Field that indicates how missing values will be filled.
      paddingValue:
        type: number
        format: float
        description: Field that's required when fillNAMethod is Fixed.
  Multivariate.AnomalyDetectionModel:
    type: object
    description: Response of getting a model.
    properties:
      modelId:
        $ref: '#/definitions/Azure.Core.uuid'
        description: Model identifier.
      createdTime:
        type: string
        format: date-time
        description: Date and time (UTC) when the model was created.
      lastUpdatedTime:
        type: string
        format: date-time
        description: Date and time (UTC) when the model was last updated.
      modelInfo:
        $ref: '#/definitions/Multivariate.ModelInfo'
        description: >-
          Training result of a model, including its status, errors, and
          diagnostics

          information.
    required:
      - modelId
      - createdTime
      - lastUpdatedTime
  Multivariate.AnomalyInterpretation:
    type: object
    description: Interpretation of the anomalous time stamp.
    properties:
      variable:
        type: string
        description: Variable.
      contributionScore:
        type: number
        format: float
        description: >-
          This score shows the percentage that contributes to the anomalous time
          stamp. It's a

          number between 0 and 1.
      correlationChanges:
        $ref: '#/definitions/Multivariate.CorrelationChanges'
        description: Correlation changes among the anomalous variables.
  Multivariate.AnomalyState:
    type: object
    description: Anomaly status and information.
    properties:
      timestamp:
        type: string
        format: date-time
        description: Time stamp for this anomaly.
      value:
        $ref: '#/definitions/Multivariate.AnomalyValue'
        description: Detailed value of this anomalous time stamp.
      errors:
        type: array
        description: Error message for the current time stamp.
        items:
          $ref: '#/definitions/Multivariate.ErrorResponse'
        x-ms-identifiers: []
    required:
      - timestamp
  Multivariate.AnomalyValue:
    type: object
    description: Detailed information of the anomalous time stamp.
    properties:
      isAnomaly:
        type: boolean
        description: True if an anomaly is detected at the current time stamp.
      severity:
        type: number
        format: float
        description: >-
          Indicates the significance of the anomaly. The higher the severity,
          the more

          significant the anomaly is.
        minimum: 0
        maximum: 1
      score:
        type: number
        format: float
        description: >-
          Raw anomaly score of severity, to help indicate the degree of
          abnormality.
        minimum: 0
        maximum: 2
      interpretation:
        type: array
        description: Interpretation of this anomalous time stamp.
        items:
          $ref: '#/definitions/Multivariate.AnomalyInterpretation'
        x-ms-identifiers: []
    required:
      - isAnomaly
      - severity
      - score
  Multivariate.CorrelationChanges:
    type: object
    description: Correlation changes among the anomalous variables.
    properties:
      changedVariables:
        type: array
        description: Correlated variables that have correlation changes under an anomaly.
        items:
          type: string
  Multivariate.DataSchema:
    type: string
    description: Data schema of the input data source. The default is OneTable.
    enum:
      - OneTable
      - MultiTable
    x-ms-enum:
      name: DataSchema
      modelAsString: true
      values:
        - name: OneTable
          value: OneTable
          description: >-
            OneTable means that your input data is in one CSV file, which
            contains one time stamp column and several variable columns. The
            default DataSchema value is OneTable.
        - name: MultiTable
          value: MultiTable
          description: >-
            MultiTable means that your input data is separated in multiple CSV
            files. Each file contains one time stamp column and one variable
            column, and the CSV file name should indicate the name of the
            variable. The default DataSchema value is OneTable.
  Multivariate.DiagnosticsInfo:
    type: object
    description: Diagnostics information to help inspect the states of a model or variable.
    properties:
      modelState:
        $ref: '#/definitions/Multivariate.ModelState'
        description: Model status.
      variableStates:
        type: array
        description: Variable status.
        items:
          $ref: '#/definitions/Multivariate.VariableState'
        x-ms-identifiers: []
  Multivariate.ErrorResponse:
    type: object
    description: Error information that the API returned.
    properties:
      code:
        type: string
        description: Error code.
      message:
        type: string
        description: Message that explains the error that the service reported.
    required:
      - code
      - message
  Multivariate.FillNAMethod:
    type: string
    description: Field that indicates how missing values will be filled.
    enum:
      - Previous
      - Subsequent
      - Linear
      - Zero
      - Fixed
    x-ms-enum:
      name: FillNAMethod
      modelAsString: true
  Multivariate.ModelInfo:
    type: object
    description: |-
      Training result of a model, including its status, errors, and diagnostics
      information.
    properties:
      dataSource:
        type: string
        format: uri
        description: >-
          Source link to the input data to indicate an accessible Azure Storage
          URI.

          It either points to an Azure Blob Storage folder or points to a CSV
          file in

          Azure Blob Storage, based on your data schema selection.
      dataSchema:
        $ref: '#/definitions/Multivariate.DataSchema'
        description: |-
          Data schema of the input data source. The default
          is OneTable.
      startTime:
        type: string
        format: date-time
        description: |-
          Start date/time of training data, which should be
          in ISO 8601 format.
      endTime:
        type: string
        format: date-time
        description: |-
          End date/time of training data, which should be
          in ISO 8601 format.
      displayName:
        type: string
        description: |-
          Display name of the model. Maximum length is 24
          characters.
        maxLength: 24
      slidingWindow:
        type: integer
        format: int32
        description: |-
          Number of previous time stamps that will be used to
          detect whether the time stamp is an anomaly or not.
      alignPolicy:
        $ref: '#/definitions/Multivariate.AlignPolicy'
        description: Manner of aligning multiple variables.
      status:
        $ref: '#/definitions/Multivariate.ModelStatus'
        description: Model status.
        readOnly: true
      errors:
        type: array
        description: Error messages after failure to create a model.
        items:
          $ref: '#/definitions/Multivariate.ErrorResponse'
        readOnly: true
        x-ms-identifiers: []
      diagnosticsInfo:
        $ref: '#/definitions/Multivariate.DiagnosticsInfo'
        description: >-
          Diagnostics information to help inspect the states of a model or
          variable.
        readOnly: true
    required:
      - dataSource
      - startTime
      - endTime
  Multivariate.ModelList:
    type: object
    description: Response of listing models.
    properties:
      models:
        type: array
        description: List of models.
        items:
          $ref: '#/definitions/Multivariate.AnomalyDetectionModel'
        x-ms-identifiers: []
      currentCount:
        type: integer
        format: int32
        description: Number of trained multivariate models.
      maxCount:
        type: integer
        format: int32
        description: >-
          Maximum number of models that can be trained for this Anomaly Detector
          resource.
      nextLink:
        type: string
        description: Link to fetch more models.
    required:
      - models
      - currentCount
      - maxCount
  Multivariate.ModelState:
    type: object
    description: Model status.
    properties:
      epochIds:
        type: array
        description: |-
          Number of passes of the entire training dataset that the
          algorithm has completed.
        items:
          type: integer
          format: int32
      trainLosses:
        type: array
        description: >-
          List of metrics used to assess how the model fits the training data
          for each

          epoch.
        items:
          type: number
          format: float
      validationLosses:
        type: array
        description: >-
          List of metrics used to assess how the model fits the validation set
          for each

          epoch.
        items:
          type: number
          format: float
      latenciesInSeconds:
        type: array
        description: Latency for each epoch.
        items:
          type: number
          format: float
  Multivariate.ModelStatus:
    type: string
    enum:
      - CREATED
      - RUNNING
      - READY
      - FAILED
    x-ms-enum:
      name: ModelStatus
      modelAsString: true
      values:
        - name: Created
          value: CREATED
          description: >-
            The model has been created. Training has been scheduled but not yet
            started.
        - name: Running
          value: RUNNING
          description: The model is being trained.
        - name: Ready
          value: READY
          description: >-
            The model has been trained and is ready to be used for anomaly
            detection.
        - name: Failed
          value: FAILED
          description: The model training failed.
  Multivariate.MultivariateBatchDetectionOptions:
    type: object
    description: >-
      Detection request for batch inference. This is an asynchronous inference
      that

      will need another API to get detection results.
    properties:
      dataSource:
        type: string
        format: uri
        description: >-
          Source link to the input data to indicate an accessible Azure Storage
          URI.

          It either points to an Azure Blob Storage folder or points to a CSV
          file in

          Azure Blob Storage, based on your data schema selection. The data
          schema should

          be exactly the same as those used in the training phase. The input
          data must

          contain at least slidingWindow entries preceding the start time of the
          data

          to be detected.
      topContributorCount:
        type: integer
        format: int32
        description: >-
          Number of top contributed variables for one anomalous time stamp in
          the response.
        default: 10
      startTime:
        type: string
        format: date-time
        description: |-
          Start date/time of data for detection, which should
          be in ISO 8601 format.
      endTime:
        type: string
        format: date-time
        description: |-
          End date/time of data for detection, which should
          be in ISO 8601 format.
    required:
      - dataSource
      - startTime
      - endTime
  Multivariate.MultivariateBatchDetectionResultSummary:
    type: object
    description: Multivariate anomaly detection status.
    properties:
      status:
        $ref: '#/definitions/Multivariate.MultivariateBatchDetectionStatus'
        description: Status of detection results.
      errors:
        type: array
        description: Error message when detection fails.
        items:
          $ref: '#/definitions/Multivariate.ErrorResponse'
        x-ms-identifiers: []
      variableStates:
        type: array
        description: Variable status.
        items:
          $ref: '#/definitions/Multivariate.VariableState'
        x-ms-identifiers: []
      setupInfo:
        $ref: '#/definitions/Multivariate.MultivariateBatchDetectionOptions'
        description: >-
          Detection request for batch inference. This is an asynchronous
          inference that

          will need another API to get detection results.
    required:
      - status
      - setupInfo
  Multivariate.MultivariateBatchDetectionStatus:
    type: string
    enum:
      - CREATED
      - RUNNING
      - READY
      - FAILED
    x-ms-enum:
      name: MultivariateBatchDetectionStatus
      modelAsString: true
      values:
        - name: Created
          value: CREATED
        - name: Running
          value: RUNNING
        - name: Ready
          value: READY
        - name: Failed
          value: FAILED
  Multivariate.MultivariateDetectionResult:
    type: object
    description: Detection results for the resultId value.
    properties:
      resultId:
        $ref: '#/definitions/Azure.Core.uuid'
        description: >-
          Result identifier that's used to fetch the results of an inference
          call.
      summary:
        $ref: '#/definitions/Multivariate.MultivariateBatchDetectionResultSummary'
        description: Multivariate anomaly detection status.
      results:
        type: array
        description: Detection result for each time stamp.
        items:
          $ref: '#/definitions/Multivariate.AnomalyState'
        x-ms-identifiers: []
    required:
      - resultId
      - summary
      - results
  Multivariate.MultivariateLastDetectionOptions:
    type: object
    description: Request of the last detection.
    properties:
      variables:
        type: array
        description: >-
          Contains the inference data, including the name, time stamps (ISO
          8601), and

          values of variables.
        items:
          $ref: '#/definitions/Multivariate.VariableValues'
        x-ms-identifiers: []
      topContributorCount:
        type: integer
        format: int32
        description: |-
          Number of top contributed
          variables for one anomalous time stamp in the response. The default is
          10.
        default: 10
    required:
      - variables
  Multivariate.MultivariateLastDetectionResult:
    type: object
    description: Results of the last detection.
    properties:
      variableStates:
        type: array
        description: Variable status.
        items:
          $ref: '#/definitions/Multivariate.VariableState'
        x-ms-identifiers: []
      results:
        type: array
        description: Anomaly status and information.
        items:
          $ref: '#/definitions/Multivariate.AnomalyState'
        x-ms-identifiers: []
  Multivariate.ResponseError:
    type: object
    description: Error response.
    properties:
      code:
        type: string
        description: Error code.
      message:
        type: string
        description: Message that explains the error that the service reported.
    required:
      - code
      - message
  Multivariate.VariableState:
    type: object
    description: Variable status.
    properties:
      variable:
        type: string
        description: Variable name in variable states.
      filledNARatio:
        type: number
        format: float
        description: Proportion of missing values that need to be filled by fillNAMethod.
        minimum: 0
        maximum: 1
      effectiveCount:
        type: integer
        format: int32
        description: Number of effective data points before fillNAMethod is applied.
      firstTimestamp:
        type: string
        format: date-time
        description: First valid time stamp with a value of input data.
      lastTimestamp:
        type: string
        format: date-time
        description: Last valid time stamp with a value of input data.
  Multivariate.VariableValues:
    type: object
    description: Variable values.
    properties:
      variable:
        type: string
        description: Variable name of the last detection request.
      timestamps:
        type: array
        description: Time stamps of the last detection request.
        items:
          type: string
      values:
        type: array
        description: Values of variables.
        items:
          type: number
          format: float
    required:
      - variable
      - timestamps
      - values
  Univariate.AnomalyDetectorError:
    type: object
    description: Error information that the API returned.
    properties:
      code:
        $ref: '#/definitions/Univariate.AnomalyDetectorErrorCodes'
        description: Error code.
      message:
        type: string
        description: Message that explains the error that the service reported.
    required:
      - code
      - message
  Univariate.AnomalyDetectorErrorCodes:
    type: string
    enum:
      - InvalidCustomInterval
      - BadArgument
      - InvalidGranularity
      - InvalidPeriod
      - InvalidModelArgument
      - InvalidSeries
      - InvalidJsonFormat
      - RequiredGranularity
      - RequiredSeries
      - InvalidImputeMode
      - InvalidImputeFixedValue
    x-ms-enum:
      name: AnomalyDetectorErrorCodes
      modelAsString: true
  Univariate.ImputeMode:
    type: string
    enum:
      - auto
      - previous
      - linear
      - fixed
      - zero
      - notFill
    x-ms-enum:
      name: ImputeMode
      modelAsString: true
      values:
        - name: Auto
          value: auto
        - name: Previous
          value: previous
        - name: Linear
          value: linear
        - name: Fixed
          value: fixed
        - name: Zero
          value: zero
        - name: NotFill
          value: notFill
  Univariate.TimeGranularity:
    type: string
    enum:
      - yearly
      - monthly
      - weekly
      - daily
      - hourly
      - minutely
      - secondly
      - microsecond
      - none
    x-ms-enum:
      name: TimeGranularity
      modelAsString: true
      values:
        - name: Yearly
          value: yearly
        - name: Monthly
          value: monthly
        - name: Weekly
          value: weekly
        - name: Daily
          value: daily
        - name: Hourly
          value: hourly
        - name: PerMinute
          value: minutely
        - name: PerSecond
          value: secondly
        - name: Microsecond
          value: microsecond
        - name: None
          value: none
  Univariate.TimeSeriesPoint:
    type: object
    description: Definition of input time series points.
    properties:
      timestamp:
        type: string
        format: date-time
        description: >-
          Argument that indicates the time stamp of a data point (ISO8601
          format).
      value:
        type: number
        format: float
        description: Measurement of that point.
    required:
      - value
  Univariate.UnivariateChangePointDetectionOptions:
    type: object
    description: Request of change point detection.
    properties:
      series:
        type: array
        description: >-
          Time series data points. Points should be sorted by time stamp in
          ascending

          order to match the change point detection result.
        items:
          $ref: '#/definitions/Univariate.TimeSeriesPoint'
        x-ms-identifiers: []
      granularity:
        $ref: '#/definitions/Univariate.TimeGranularity'
        description: Granularity is used to verify whether the input series is valid.
      customInterval:
        type: integer
        format: int32
        description: >-
          A custom interval is used to set a nonstandard time interval. For
          example, if the

          series is 5 minutes, the request can be set as
          {"granularity":"minutely",

          "customInterval":5}.
      period:
        type: integer
        format: int32
        description: >-
          Argument that indicates the periodic value of a time series. If the
          value is null or

          not present, the API will determine the period automatically.
      stableTrendWindow:
        type: integer
        format: int32
        description: >-
          Argument that indicates an advanced model parameter. A default
          stableTrendWindow value will

          be used in detection.
      threshold:
        type: number
        format: float
        description: >-
          Argument that indicates an advanced model parameter between 0.0 and
          1.0. The lower the

          value is, the larger the trend error is, which means less change point
          will

          be accepted.
    required:
      - series
      - granularity
  Univariate.UnivariateChangePointDetectionResult:
    type: object
    description: Response of change point detection.
    properties:
      period:
        type: integer
        format: int32
        description: >-
          Frequency extracted from the series. Zero means no recurrent pattern
          has been

          found.
        readOnly: true
      isChangePoint:
        type: array
        description: >-
          Change point properties for each input point. True means

          an anomaly (either negative or positive) has been detected. The index
          of the

          array is consistent with the input series.
        items:
          type: boolean
      confidenceScores:
        type: array
        description: Change point confidence of each point.
        items:
          type: number
          format: float
  Univariate.UnivariateDetectionOptions:
    type: object
    description: Request of the entire or last anomaly detection.
    properties:
      series:
        type: array
        description: >-
          Time series data points. Points should be sorted by time stamp in
          ascending

          order to match the anomaly detection result. If the data is not sorted

          correctly or there's a duplicated time stamp, the API won't work. In
          such

          a case, an error message is returned.
        items:
          $ref: '#/definitions/Univariate.TimeSeriesPoint'
        x-ms-identifiers: []
      granularity:
        $ref: '#/definitions/Univariate.TimeGranularity'
        description: >-
          Argument that indicates time granularity. If granularity is not
          present, the value

          is none by default. If granularity is none, the time stamp property in
          the time

          series point can be absent.
      customInterval:
        type: integer
        format: int32
        description: >-
          A custom interval is used to set a nonstandard time interval. For
          example, if the

          series is 5 minutes, the request can be set as
          {"granularity":"minutely",

          "customInterval":5}.
      period:
        type: integer
        format: int32
        description: >-
          Argument that indicates the periodic value of a time series. If the
          value is null or

          is not present, the API determines the period automatically.
      maxAnomalyRatio:
        type: number
        format: float
        description: >-
          Argument that indicates an advanced model parameter. It's the maximum
          anomaly ratio in a time series.
      sensitivity:
        type: integer
        format: int32
        description: >-
          Argument that indicates an advanced model parameter between 0 and 99.
          The lower the value

          is, the larger the margin value is, which means fewer anomalies will
          be

          accepted.
      imputeMode:
        $ref: '#/definitions/Univariate.ImputeMode'
        description: >-
          Specifies how to deal with missing values in the input series. It's
          used

          when granularity is not "none".
      imputeFixedValue:
        type: number
        format: float
        description: |-
          Specifies the value to fill. It's used when granularity is not "none"
          and imputeMode is "fixed".
    required:
      - series
  Univariate.UnivariateEntireDetectionResult:
    type: object
    description: Response of the entire anomaly detection.
    properties:
      period:
        type: integer
        format: int32
        description: >-
          Frequency extracted from the series. Zero means no recurrent pattern
          has been

          found.
      expectedValues:
        type: array
        description: |-
          Expected value for each input point. The index of the
          array is consistent with the input series.
        items:
          type: number
          format: float
      upperMargins:
        type: array
        description: >-
          Upper margin of each input point. UpperMargin is used to

          calculate upperBoundary, which is equal to expectedValue + (100 -

          marginScale)*upperMargin. Anomalies in the response can be filtered by

          upperBoundary and lowerBoundary. Adjusting the marginScale value can
          help filter less

          significant anomalies on the client side. The index of the array is

          consistent with the input series.
        items:
          type: number
          format: float
      lowerMargins:
        type: array
        description: >-
          Lower margin of each input point. LowerMargin is used to

          calculate lowerBoundary, which is equal to expectedValue - (100 -

          marginScale)*lowerMargin. Points between the boundary can be marked as
          normal

          ones on the client side. The index of the array is consistent with the
          input

          series.
        items:
          type: number
          format: float
      isAnomaly:
        type: array
        description: >-
          Anomaly properties for each input point. True means an

          anomaly (either negative or positive) has been detected. The index of
          the array

          is consistent with the input series.
        items:
          type: boolean
      isNegativeAnomaly:
        type: array
        description: >-
          Anomaly status in a negative direction for each input

          point. True means a negative anomaly has been detected. A negative
          anomaly

          means the point is detected as an anomaly and its real value is
          smaller than

          the expected one. The index of the array is consistent with the input
          series.
        items:
          type: boolean
      isPositiveAnomaly:
        type: array
        description: >-
          Anomaly status in a positive direction for each input

          point. True means a positive anomaly has been detected. A positive
          anomaly

          means the point is detected as an anomaly and its real value is larger
          than the

          expected one. The index of the array is consistent with the input
          series.
        items:
          type: boolean
      severity:
        type: array
        description: |-
          Severity score for each input point. The larger the value is, the more
          severe the anomaly is. For normal points, the severity is always 0.
        items:
          type: number
          format: float
    required:
      - period
      - expectedValues
      - upperMargins
      - lowerMargins
      - isAnomaly
      - isNegativeAnomaly
      - isPositiveAnomaly
  Univariate.UnivariateLastDetectionResult:
    type: object
    description: Response of the last anomaly detection.
    properties:
      period:
        type: integer
        format: int32
        description: >-
          Frequency extracted from the series. Zero means no recurrent pattern
          has been

          found.
      suggestedWindow:
        type: integer
        format: int32
        description: Suggested input series points needed for detecting the latest point.
      expectedValue:
        type: number
        format: float
        description: Expected value of the latest point.
      upperMargin:
        type: number
        format: float
        description: >-
          Upper margin of the latest point. UpperMargin is used to calculate

          upperBoundary, which is equal to expectedValue + (100 -
          marginScale)*upperMargin.

          If the value of latest point is between upperBoundary and
          lowerBoundary, it

          should be treated as a normal value. Adjusting the marginScale value
          enables the anomaly

          status of the latest point to be changed.
      lowerMargin:
        type: number
        format: float
        description: >-
          Lower margin of the latest point. LowerMargin is used to calculate

          lowerBoundary, which is equal to expectedValue - (100 -
          marginScale)*lowerMargin.
      isAnomaly:
        type: boolean
        description: >-
          Anomaly status of the latest point. True means the latest point is an
          anomaly,

          either in the negative direction or in the positive direction.
      isNegativeAnomaly:
        type: boolean
        description: >-
          Anomaly status of the latest point in a negative direction. True means
          the latest

          point is an anomaly and its real value is smaller than the expected
          one.
      isPositiveAnomaly:
        type: boolean
        description: >-
          Anomaly status of the latest point in a positive direction. True means
          the latest

          point is an anomaly and its real value is larger than the expected
          one.
      severity:
        type: number
        format: float
        description: >-
          Severity score for the last input point. The larger the value is, the
          more

          severe the anomaly is. For normal points, the severity is always 0.
    required:
      - period
      - suggestedWindow
      - expectedValue
      - upperMargin
      - lowerMargin
      - isAnomaly
      - isNegativeAnomaly
      - isPositiveAnomaly
parameters:
  Azure.Core.SkipQueryParameter:
    name: skip
    in: query
    description: The number of result items to skip.
    required: false
    type: integer
    format: int32
    default: 0
    x-ms-parameter-location: method
  Azure.Core.TopQueryParameter:
    name: top
    in: query
    description: The number of result items to return.
    required: false
    type: integer
    format: int32
    x-ms-parameter-location: method