Microsoft Azure Timeseries API

swagger: '2.0'
info:
  version: 2020-08-01-preview
  title: Microsoft Azure AccessControlClient AccessConnector Timeseries API
schemes:
- https
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.
      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:
  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.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.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.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.AnomalyDetectorErrorCodes:
    type: string
    enum:
    - InvalidCustomInterval
    - BadArgument
    - InvalidGranularity
    - InvalidPeriod
    - InvalidModelArgument
    - InvalidSeries
    - InvalidJsonFormat
    - RequiredGranularity
    - RequiredSeries
    - InvalidImputeMode
    - InvalidImputeFixedValue
    x-ms-enum:
      name: AnomalyDetectorErrorCodes
      modelAsString: true
  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: '#/definitions/Univariate.AnomalyDetectorErrorCodes'
        description: Error code.
      message:
        type: string
        description: Message that explains the error that the service reported.
    required:
    - code
    - message
  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.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
x-ms-parameterized-host:
  hostTemplate: '{endpoint}'
  useSchemePrefix: false
  parameters:
  - $ref: '#/parameters/Endpoint'