Struct google_api_proto::google::cloud::timeseriesinsights::v1::ForecastParams
source · pub struct ForecastParams {
pub noise_threshold: Option<f64>,
pub seasonality_hint: i32,
pub horizon_duration: Option<Duration>,
}
Expand description
Parameters that control the sensitivity and other options for the time series forecast.
Fields§
§noise_threshold: Option<f64>
Optional. Penalize variations between the actual and forecasted values smaller than this. For more information about how this parameter affects the score, see the anomalyScore formula.
Intuitively, anomaly scores summarize how statistically significant the change between the actual and forecasted value is compared with what we expect the change to be (see expectedDeviation). However, in practice, depending on the application, changes smaller than certain absolute values, while statistically significant, may not be important.
This parameter allows us to penalize such low absolute value changes.
Must be in the (0.0, inf) range.
If unspecified, it defaults to 0.000001.
seasonality_hint: i32
Optional. Specifying any known seasonality/periodicity in the time series for the slices we will analyze can improve the quality of the results.
If unsure, simply leave it unspecified by not setting a value for this field.
If your time series has multiple seasonal patterns, then set it to the most granular one (e.g. if it has daily and weekly patterns, set this to DAILY).
horizon_duration: Option<Duration>
Optional. The length of the returned [forecasted timeseries][EvaluatedSlice.forecast].
This duration is currently capped at 100 x [granularity][google.cloud.timeseriesinsights.v1.TimeseriesParams.granularity].
Example: If the detection point is set to “2020-12-27T00:00:00Z”, the [granularity][google.cloud.timeseriesinsights.v1.TimeseriesParams.granularity] to “3600s” and the horizon_duration to “10800s”, then we will generate 3 time series points (from “2020-12-27T01:00:00Z” to “2020-12-27T04:00:00Z”), for which we will return their forecasted values.
Note: The horizon time is only used for forecasting not for anormaly detection. To detect anomalies for multiple points of time, simply send multiple queries with those as [detectionTime][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.detection_time].
Implementations§
source§impl ForecastParams
impl ForecastParams
sourcepub fn seasonality_hint(&self) -> Period
pub fn seasonality_hint(&self) -> Period
Returns the enum value of seasonality_hint
, or the default if the field is set to an invalid enum value.
sourcepub fn set_seasonality_hint(&mut self, value: Period)
pub fn set_seasonality_hint(&mut self, value: Period)
Sets seasonality_hint
to the provided enum value.
sourcepub fn noise_threshold(&self) -> f64
pub fn noise_threshold(&self) -> f64
Returns the value of noise_threshold
, or the default value if noise_threshold
is unset.
Trait Implementations§
source§impl Clone for ForecastParams
impl Clone for ForecastParams
source§fn clone(&self) -> ForecastParams
fn clone(&self) -> ForecastParams
1.0.0 · source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source
. Read moresource§impl Debug for ForecastParams
impl Debug for ForecastParams
source§impl Default for ForecastParams
impl Default for ForecastParams
source§impl Message for ForecastParams
impl Message for ForecastParams
source§fn encoded_len(&self) -> usize
fn encoded_len(&self) -> usize
source§fn encode(&self, buf: &mut impl BufMut) -> Result<(), EncodeError>where
Self: Sized,
fn encode(&self, buf: &mut impl BufMut) -> Result<(), EncodeError>where
Self: Sized,
source§fn encode_to_vec(&self) -> Vec<u8>where
Self: Sized,
fn encode_to_vec(&self) -> Vec<u8>where
Self: Sized,
source§fn encode_length_delimited(
&self,
buf: &mut impl BufMut,
) -> Result<(), EncodeError>where
Self: Sized,
fn encode_length_delimited(
&self,
buf: &mut impl BufMut,
) -> Result<(), EncodeError>where
Self: Sized,
source§fn encode_length_delimited_to_vec(&self) -> Vec<u8>where
Self: Sized,
fn encode_length_delimited_to_vec(&self) -> Vec<u8>where
Self: Sized,
source§fn decode(buf: impl Buf) -> Result<Self, DecodeError>where
Self: Default,
fn decode(buf: impl Buf) -> Result<Self, DecodeError>where
Self: Default,
source§fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>where
Self: Default,
fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>where
Self: Default,
source§fn merge(&mut self, buf: impl Buf) -> Result<(), DecodeError>where
Self: Sized,
fn merge(&mut self, buf: impl Buf) -> Result<(), DecodeError>where
Self: Sized,
self
. Read moresource§fn merge_length_delimited(&mut self, buf: impl Buf) -> Result<(), DecodeError>where
Self: Sized,
fn merge_length_delimited(&mut self, buf: impl Buf) -> Result<(), DecodeError>where
Self: Sized,
self
.source§impl PartialEq for ForecastParams
impl PartialEq for ForecastParams
source§fn eq(&self, other: &ForecastParams) -> bool
fn eq(&self, other: &ForecastParams) -> bool
self
and other
values to be equal, and is used
by ==
.impl Copy for ForecastParams
impl StructuralPartialEq for ForecastParams
Auto Trait Implementations§
impl Freeze for ForecastParams
impl RefUnwindSafe for ForecastParams
impl Send for ForecastParams
impl Sync for ForecastParams
impl Unpin for ForecastParams
impl UnwindSafe for ForecastParams
Blanket Implementations§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
§impl<T> Instrument for T
impl<T> Instrument for T
§fn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
§fn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
source§impl<T> IntoRequest<T> for T
impl<T> IntoRequest<T> for T
source§fn into_request(self) -> Request<T>
fn into_request(self) -> Request<T>
T
in a tonic::Request