anomalydetector

package
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 Go to latest Published: Apr 1, 2021 License: MIT Imports: 9 Imported by: 219

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 kinds of mode, one is for stateless using, another is for stateful using. In stateless mode, there are three functionalities. Entire Detect is for detecting the whole series with model trained by the time series, Last Detect is detecting last point with model trained by points before. ChangePoint Detect is for detecting trend changes in time series. In stateful mode, user can store time series, the stored time series will be used for detection anomalies. Under this mode, user can still use the above three functionalities by only giving a time range without preparing time series in client side. Besides the above three functionalities, stateful model also provide group based detection and labeling service. By leveraging labeling service 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, this detection will find inconsistency ones in a set of time series. By using anomaly detector service, business customers can discover incidents and establish a logic flow for root cause analysis.

Index

Constants

This section is empty.

Variables

This section is empty.

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 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) DetectChangePoint 

func (client BaseClient) DetectChangePoint(ctx context.Context, body ChangePointDetectRequest) (result ChangePointDetectResponse, err error)

DetectChangePoint evaluate change point score of every series point Parameters: body - time series points and granularity is needed. Advanced model parameters can also be set in the request if needed.

func (BaseClient) DetectChangePointPreparer 

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

DetectChangePointPreparer prepares the DetectChangePoint request.

func (BaseClient) DetectChangePointResponder 

func (client BaseClient) DetectChangePointResponder(resp *http.Response) (result ChangePointDetectResponse, err error)

DetectChangePointResponder handles the response to the DetectChangePoint request. The method always closes the http.Response Body.

func (BaseClient) DetectChangePointSender 

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

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

func (BaseClient) DetectEntireSeries 

func (client BaseClient) DetectEntireSeries(ctx context.Context, body DetectRequest) (result EntireDetectResponse, err error)

DetectEntireSeries 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) DetectEntireSeriesPreparer 

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

DetectEntireSeriesPreparer prepares the DetectEntireSeries request.

func (BaseClient) DetectEntireSeriesResponder 

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

DetectEntireSeriesResponder handles the response to the DetectEntireSeries request. The method always closes the http.Response Body.

func (BaseClient) DetectEntireSeriesSender 

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

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

func (BaseClient) DetectLastPoint 

func (client BaseClient) DetectLastPoint(ctx context.Context, body DetectRequest) (result LastDetectResponse, err error)

DetectLastPoint 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) DetectLastPointPreparer 

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

DetectLastPointPreparer prepares the DetectLastPoint request.

func (BaseClient) DetectLastPointResponder 

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

DetectLastPointResponder handles the response to the DetectLastPoint request. The method always closes the http.Response Body.

func (BaseClient) DetectLastPointSender 

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

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

type ChangePointDetectRequest 

type ChangePointDetectRequest struct {
	// Series - Time series data points. Points should be sorted by timestamp in ascending order to match the change point detection result.
	Series *[]TimeSeriesPoint `json:"series,omitempty"`
	// Granularity - Can only be one of yearly, monthly, weekly, daily, hourly, minutely or secondly. Granularity is used for verify whether input series is valid. Possible values include: 'Yearly', 'Monthly', 'Weekly', 'Daily', 'Hourly', 'PerMinute', 'PerSecond'
	Granularity TimeGranularity `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"`
	// StableTrendWindow - Optional argument, advanced model parameter, a default stableTrendWindow will be used in detection.
	StableTrendWindow *int32 `json:"stableTrendWindow,omitempty"`
	// Threshold - Optional argument, advanced model parameter, between 0.0-1.0, the lower the value is, the larger the trend error will be which means less change point will be accepted.
	Threshold *float64 `json:"threshold,omitempty"`
}

ChangePointDetectRequest ...

type ChangePointDetectResponse 

type ChangePointDetectResponse struct {
	autorest.Response `json:"-"`
	// Period - Frequency extracted from the series, zero means no recurrent pattern has been found.
	Period *int32 `json:"period,omitempty"`
	// IsChangePoint - isChangePoint contains 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.
	IsChangePoint *[]bool `json:"isChangePoint,omitempty"`
	// ConfidenceScores - the change point confidence of each point
	ConfidenceScores *[]float64 `json:"confidenceScores,omitempty"`
}

ChangePointDetectResponse ...

type DetectRequest 

type DetectRequest 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 *[]TimeSeriesPoint `json:"series,omitempty"`
	// Granularity - Possible values include: 'Yearly', 'Monthly', 'Weekly', 'Daily', 'Hourly', 'PerMinute', 'PerSecond'
	Granularity TimeGranularity `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"`
}

DetectRequest ...

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 Error 

type Error 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"`
}

Error error information returned by the API.

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 TimeGranularity 

type TimeGranularity string

TimeGranularity enumerates the values for time granularity. 

const (
	// Daily ...
	Daily TimeGranularity = "daily"
	// Hourly ...
	Hourly TimeGranularity = "hourly"
	// Monthly ...
	Monthly TimeGranularity = "monthly"
	// PerMinute ...
	PerMinute TimeGranularity = "minutely"
	// PerSecond ...
	PerSecond TimeGranularity = "secondly"
	// Weekly ...
	Weekly TimeGranularity = "weekly"
	// Yearly ...
	Yearly TimeGranularity = "yearly"
)

func PossibleTimeGranularityValues 

func PossibleTimeGranularityValues() []TimeGranularity

PossibleTimeGranularityValues returns an array of possible values for the TimeGranularity const type.

type TimeSeriesPoint 

type TimeSeriesPoint 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"`
}

TimeSeriesPoint ...

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