Struct google_api_proto::google::cloud::timeseriesinsights::v1::EvaluatedSlice

pub struct EvaluatedSlice {
    pub dimensions: Vec<PinnedDimension>,
    pub detection_point_actual: Option<f64>,
    pub detection_point_forecast: Option<f64>,
    pub expected_deviation: Option<f64>,
    pub anomaly_score: Option<f64>,
    pub history: Option<Timeseries>,
    pub forecast: Option<Timeseries>,
    pub status: Option<Status>,
}

Forecast result for a given slice.

Fields

dimensions: Vec<PinnedDimension>

Values for all categorical dimensions that uniquely identify this slice.

detection_point_actual: Option<f64>

The actual value at the detection time (see [detectionTime][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.detection_time]).

NOTE: This value can be an estimate, so it should not be used as a source of truth.

detection_point_forecast: Option<f64>

The expected value at the detection time, which is obtained by forecasting on the historical time series.

expected_deviation: Option<f64>

How much our forecast model expects the detection point actual will deviate from its forecasted value based on how well it fit the input time series.

In general, we expect the detectionPointActual to be in the [detectionPointForecast - expectedDeviation, detectionPointForecast + expectedDeviation] range. The more the actual value is outside this range, the more statistically significant the anomaly is.

The expected deviation is always positive.

anomaly_score: Option<f64>

Summarizes how significant the change between the actual and forecasted detection points are compared with the historical patterns observed on the [history][google.cloud.timeseriesinsights.v1.EvaluatedSlice.history] time series.

Defined as |a - f| / (e + nt), where:

• a is the [detectionPointActual][google.cloud.timeseriesinsights.v1.EvaluatedSlice.detection_point_actual].
• f is the [detectionPointForecast][google.cloud.timeseriesinsights.v1.EvaluatedSlice.detection_point_forecast].
• e is the [expectedDeviation][google.cloud.timeseriesinsights.v1.EvaluatedSlice.expected_deviation].
• *nt` is the [noiseThreshold][google.cloud.timeseriesinsights.v1.ForecastParams.noise_threshold].

Anomaly scores between different requests and datasets are comparable. As a guideline, the risk of a slice being an anomaly based on the anomaly score is:

• Very High if anomalyScore > 5.
• High if the anomalyScore is in the [2, 5] range.
• Medium if the anomalyScore is in the [1, 2) range.
• Low if the anomalyScore is < 1.

If there were issues evaluating this slice, then the anomaly score will be set to -1.0 and the [status][google.cloud.timeseriesinsights.v1.EvaluatedSlice.status] field will contain details on what went wrong.

history: Option<Timeseries>

The actual values in the [ [detectionTime][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.detection_time] - [forecastHistory][google.cloud.timeseriesinsights.v1.TimeseriesParams.forecast_history], [detectionTime][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.detection_time] ] time range.

NOTE: This field is only populated if [returnTimeseries][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.return_timeseries] is true.

forecast: Option<Timeseries>

The forecasted values in the [ [detectionTime][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.detection_time] + [granularity][google.cloud.timeseriesinsights.v1.TimeseriesParams.granularity], [forecastParams.horizonTime][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.forecast_params] ] time range.

NOTE: This field is only populated if [returnTimeseries][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.return_timeseries] is true.

status: Option<Status>

Evaluation status. Contains an error message if the anomalyScore is < 0.

Possible error messages:

• “Time series too sparse”: The returned time series for this slice did not contain enough data points (we require a minimum of 10).
• “Not enough recent time series points”: The time series contains the minimum of 10 points, but there are not enough close in time to the detection point.
• “Missing detection point data”: There were not events to be aggregated within the \[detectionTime, detectionTime + granularity\] time interval, so we don’t have an actual value with which we can compare our prediction.
• “Data retrieval error”: We failed to retrieve the time series data for this slice and could not evaluate it successfully. Should be a transient error.
• “Internal server error”: Internal unexpected error.

Implementations

impl EvaluatedSlice

pub fn detection_point_actual(&self) -> f64

Returns the value of detection_point_actual, or the default value if detection_point_actual is unset.

pub fn detection_point_forecast(&self) -> f64

Returns the value of detection_point_forecast, or the default value if detection_point_forecast is unset.

pub fn expected_deviation(&self) -> f64

Returns the value of expected_deviation, or the default value if expected_deviation is unset.

pub fn anomaly_score(&self) -> f64

Returns the value of anomaly_score, or the default value if anomaly_score is unset.

Trait Implementations

impl Clone for EvaluatedSlice

fn clone(&self) -> EvaluatedSlice

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for EvaluatedSlice

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for EvaluatedSlice

fn default() -> Self

Returns the “default value” for a type.

impl Message for EvaluatedSlice

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode(&self, buf: &mut impl BufMut) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited( &self, buf: &mut impl BufMut, ) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for EvaluatedSlice

fn eq(&self, other: &EvaluatedSlice) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for EvaluatedSlice