Microsoft Azure Timeseries API

openapi: 3.2.0
info:
  version: 2020-08-01-preview
  title: Microsoft Azure AccessControlClient AccessConnector Timeseries API
tags:
- name: Timeseries
paths:
  /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.
      responses:
        '200':
          description: The request has succeeded.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/Univariate.UnivariateChangePointDetectionResult'
        default:
          description: An unexpected error response.
          headers:
            x-ms-error-code:
              description: Error code.
              schema:
                type: string
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/Univariate.AnomalyDetectorError'
      x-ms-examples:
        Univariate detection of a change point:
          $ref: ./examples/ChangePointDetect.json
      tags:
      - Timeseries
      requestBody:
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/Univariate.UnivariateChangePointDetectionOptions'
        description: Method of univariate anomaly detection.
        required: true
  /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.
      responses:
        '200':
          description: The request has succeeded.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/Univariate.UnivariateEntireDetectionResult'
        default:
          description: An unexpected error response.
          headers:
            x-ms-error-code:
              description: Error code.
              schema:
                type: string
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/Univariate.AnomalyDetectorError'
      x-ms-examples:
        Univariate detect entire series:
          $ref: ./examples/EntireDetect.json
      tags:
      - Timeseries
      requestBody:
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/Univariate.UnivariateDetectionOptions'
        description: Method of univariate anomaly detection.
        required: true
  /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.
      responses:
        '200':
          description: The request has succeeded.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/Univariate.UnivariateLastDetectionResult'
        default:
          description: An unexpected error response.
          headers:
            x-ms-error-code:
              description: Error code.
              schema:
                type: string
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/Univariate.AnomalyDetectorError'
      x-ms-examples:
        Detect univariate last point:
          $ref: ./examples/LastDetect.json
      tags:
      - Timeseries
      requestBody:
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/Univariate.UnivariateDetectionOptions'
        description: Method of univariate anomaly detection.
        required: true
components:
  schemas:
    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.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
    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.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.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: '#/components/schemas/Univariate.TimeSeriesPoint'
          x-ms-identifiers: []
        granularity:
          $ref: '#/components/schemas/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: '#/components/schemas/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.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: '#/components/schemas/Univariate.TimeSeriesPoint'
          x-ms-identifiers: []
        granularity:
          $ref: '#/components/schemas/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.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.AnomalyDetectorError:
      type: object
      description: Error information that the API returned.
      properties:
        code:
          $ref: '#/components/schemas/Univariate.AnomalyDetectorErrorCodes'
          description: Error code.
        message:
          type: string
          description: Message that explains the error that the service reported.
      required:
      - code
      - message
x-ms-parameterized-host:
  hostTemplate: '{endpoint}'
  useSchemePrefix: false
  parameters:
  - $ref: '#/components/parameters/Endpoint'