anomalydetector

Documentation

Overview

Package anomalydetector implements the Azure ARM Anomalydetector service API version 1.0.

The Anomaly Detector API detects anomalies automatically in time series data. It supports two functionalities, one is for detecting the whole series with model trained by the timeseries, another is detecting last point with model trained by points before. By using this service, business customers can discover incidents and establish a logic flow for root cause analysis.

Index

• func UserAgent() string
• func Version() string
• type APIError
• type BaseClient
  • func New(endpoint string) BaseClient
  • func NewWithoutDefaults(endpoint string) BaseClient
  • func (client BaseClient) EntireDetect(ctx context.Context, body Request) (result EntireDetectResponse, err error)
  • func (client BaseClient) EntireDetectPreparer(ctx context.Context, body Request) (*http.Request, error)
  • func (client BaseClient) EntireDetectResponder(resp *http.Response) (result EntireDetectResponse, err error)
  • func (client BaseClient) EntireDetectSender(req *http.Request) (*http.Response, error)
  • func (client BaseClient) LastDetect(ctx context.Context, body Request) (result LastDetectResponse, err error)
  • func (client BaseClient) LastDetectPreparer(ctx context.Context, body Request) (*http.Request, error)
  • func (client BaseClient) LastDetectResponder(resp *http.Response) (result LastDetectResponse, err error)
  • func (client BaseClient) LastDetectSender(req *http.Request) (*http.Response, error)
• type EntireDetectResponse
• type Granularity
  • func PossibleGranularityValues() []Granularity
• type LastDetectResponse
• type Point
• type Request

Functions

func UserAgent

func UserAgent() string

UserAgent returns the UserAgent string to use when sending http.Requests.

func Version

func Version() string

Version returns the semantic version (see http://semver.org) of the client.

Types

type APIError

type APIError struct {
	// Code - The error code.
	Code interface{} `json:"code,omitempty"`
	// Message - A message explaining the error reported by the service.
	Message *string `json:"message,omitempty"`
}

APIError error information returned by the API.

type BaseClient

type BaseClient struct {
	autorest.Client
	Endpoint string
}

BaseClient is the base client for Anomalydetector.

func New

func New(endpoint string) BaseClient

New creates an instance of the BaseClient client.

func NewWithoutDefaults

func NewWithoutDefaults(endpoint string) BaseClient

NewWithoutDefaults creates an instance of the BaseClient client.

func (BaseClient) EntireDetect

func (client BaseClient) EntireDetect(ctx context.Context, body Request) (result EntireDetectResponse, err error)

EntireDetect this operation generates a model using an entire series, each point is detected with the same model. With this method, points before and after a certain point are used to determine whether it is an anomaly. The entire detection can give user an overall status of the time series. Parameters: body - time series points and period if needed. Advanced model parameters can also be set in the request.

func (BaseClient) EntireDetectPreparer

func (client BaseClient) EntireDetectPreparer(ctx context.Context, body Request) (*http.Request, error)

EntireDetectPreparer prepares the EntireDetect request.

func (BaseClient) EntireDetectResponder

func (client BaseClient) EntireDetectResponder(resp *http.Response) (result EntireDetectResponse, err error)

EntireDetectResponder handles the response to the EntireDetect request. The method always closes the http.Response Body.

func (BaseClient) EntireDetectSender

func (client BaseClient) EntireDetectSender(req *http.Request) (*http.Response, error)

EntireDetectSender sends the EntireDetect request. The method will close the http.Response Body if it receives an error.

func (BaseClient) LastDetect

func (client BaseClient) LastDetect(ctx context.Context, body Request) (result LastDetectResponse, err error)

LastDetect this operation generates a model using points before the latest one. With this method, only historical points are used to determine whether the target point is an anomaly. The latest point detecting operation matches the scenario of real-time monitoring of business metrics. Parameters: body - time series points and period if needed. Advanced model parameters can also be set in the request.

func (BaseClient) LastDetectPreparer

func (client BaseClient) LastDetectPreparer(ctx context.Context, body Request) (*http.Request, error)

LastDetectPreparer prepares the LastDetect request.

func (BaseClient) LastDetectResponder

func (client BaseClient) LastDetectResponder(resp *http.Response) (result LastDetectResponse, err error)

LastDetectResponder handles the response to the LastDetect request. The method always closes the http.Response Body.

func (BaseClient) LastDetectSender

func (client BaseClient) LastDetectSender(req *http.Request) (*http.Response, error)

LastDetectSender sends the LastDetect request. The method will close the http.Response Body if it receives an error.

type EntireDetectResponse

type EntireDetectResponse struct {
	autorest.Response `json:"-"`
	// Period - Frequency extracted from the series, zero means no recurrent pattern has been found.
	Period *int32 `json:"period,omitempty"`
	// ExpectedValues - ExpectedValues contain expected value for each input point. The index of the array is consistent with the input series.
	ExpectedValues *[]float64 `json:"expectedValues,omitempty"`
	// UpperMargins - UpperMargins contain upper margin of each input point. UpperMargin is used to calculate upperBoundary, which equals to expectedValue + (100 - marginScale)*upperMargin. Anomalies in response can be filtered by upperBoundary and lowerBoundary. By adjusting marginScale value, less significant anomalies can be filtered in client side. The index of the array is consistent with the input series.
	UpperMargins *[]float64 `json:"upperMargins,omitempty"`
	// LowerMargins - LowerMargins contain lower margin of each input point. LowerMargin is used to calculate lowerBoundary, which equals to expectedValue - (100 - marginScale)*lowerMargin. Points between the boundary can be marked as normal ones in client side. The index of the array is consistent with the input series.
	LowerMargins *[]float64 `json:"lowerMargins,omitempty"`
	// IsAnomaly - IsAnomaly contains 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.
	IsAnomaly *[]bool `json:"isAnomaly,omitempty"`
	// IsNegativeAnomaly - IsNegativeAnomaly contains anomaly status in 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.
	IsNegativeAnomaly *[]bool `json:"isNegativeAnomaly,omitempty"`
	// IsPositiveAnomaly - IsPositiveAnomaly contain anomaly status in 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.
	IsPositiveAnomaly *[]bool `json:"isPositiveAnomaly,omitempty"`
}

EntireDetectResponse ...

type Granularity

type Granularity string

Granularity enumerates the values for granularity.

const (
	// Daily ...
	Daily Granularity = "daily"
	// Hourly ...
	Hourly Granularity = "hourly"
	// Minutely ...
	Minutely Granularity = "minutely"
	// Monthly ...
	Monthly Granularity = "monthly"
	// Weekly ...
	Weekly Granularity = "weekly"
	// Yearly ...
	Yearly Granularity = "yearly"
)

func PossibleGranularityValues

func PossibleGranularityValues() []Granularity

PossibleGranularityValues returns an array of possible values for the Granularity const type.

type LastDetectResponse

type LastDetectResponse struct {
	autorest.Response `json:"-"`
	// Period - Frequency extracted from the series, zero means no recurrent pattern has been found.
	Period *int32 `json:"period,omitempty"`
	// SuggestedWindow - Suggested input series points needed for detecting the latest point.
	SuggestedWindow *int32 `json:"suggestedWindow,omitempty"`
	// ExpectedValue - Expected value of the latest point.
	ExpectedValue *float64 `json:"expectedValue,omitempty"`
	// UpperMargin - Upper margin of the latest point. UpperMargin is used to calculate upperBoundary, which equals to expectedValue + (100 - marginScale)*upperMargin. If the value of latest point is between upperBoundary and lowerBoundary, it should be treated as normal value. By adjusting marginScale value, anomaly status of latest point can be changed.
	UpperMargin *float64 `json:"upperMargin,omitempty"`
	// LowerMargin - Lower margin of the latest point. LowerMargin is used to calculate lowerBoundary, which equals to expectedValue - (100 - marginScale)*lowerMargin.
	LowerMargin *float64 `json:"lowerMargin,omitempty"`
	// IsAnomaly - Anomaly status of the latest point, true means the latest point is an anomaly either in negative direction or positive direction.
	IsAnomaly *bool `json:"isAnomaly,omitempty"`
	// IsNegativeAnomaly - Anomaly status in negative direction of the latest point. True means the latest point is an anomaly and its real value is smaller than the expected one.
	IsNegativeAnomaly *bool `json:"isNegativeAnomaly,omitempty"`
	// IsPositiveAnomaly - Anomaly status in positive direction of the latest point. True means the latest point is an anomaly and its real value is larger than the expected one.
	IsPositiveAnomaly *bool `json:"isPositiveAnomaly,omitempty"`
}

LastDetectResponse ...

type Point

type Point struct {
	// Timestamp - Timestamp of a data point (ISO8601 format).
	Timestamp *date.Time `json:"timestamp,omitempty"`
	// Value - The measurement of that point, should be float.
	Value *float64 `json:"value,omitempty"`
}

Point ...

type Request

type Request struct {
	// Series - Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned.
	Series *[]Point `json:"series,omitempty"`
	// Granularity - Can only be one of yearly, monthly, weekly, daily, hourly or minutely. Granularity is used for verify whether input series is valid. Possible values include: 'Yearly', 'Monthly', 'Weekly', 'Daily', 'Hourly', 'Minutely'
	Granularity Granularity `json:"granularity,omitempty"`
	// CustomInterval - Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as {"granularity":"minutely", "customInterval":5}.
	CustomInterval *int32 `json:"customInterval,omitempty"`
	// Period - Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically.
	Period *int32 `json:"period,omitempty"`
	// MaxAnomalyRatio - Optional argument, advanced model parameter, max anomaly ratio in a time series.
	MaxAnomalyRatio *float64 `json:"maxAnomalyRatio,omitempty"`
	// Sensitivity - Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted.
	Sensitivity *int32 `json:"sensitivity,omitempty"`
}

Request ...