VictoriaMetrics/docs/anomaly-detection/components/scheduler.md

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3	Scheduler	3	docs parent weight vmanomaly-components 3	/anomaly-detection/components/scheduler.html

Scheduler

Scheduler defines how often to run and make inferences, as well as what timerange to use to train the model. Is specified in scheduler section of a config for VictoriaMetrics Anomaly Detection.

Note: Starting from v1.11.0 scheduler section in config supports multiple schedulers via aliasing.
Also, vmanomaly expects scheduler section to be named schedulers. Using old (flat) format with scheduler key is deprecated and will be removed in future versions.

schedulers:
  scheduler_periodic_1m:
    # class: "scheduler.periodic.PeriodicScheduler"
    infer_every: "1m"
    fit_every: "2m"
    fit_window: "3h"
  scheduler_periodic_5m:
    # class: "scheduler.periodic.PeriodicScheduler"
    infer_every: "5m"
    fit_every: "10m"
    fit_window: "3h"
...

Old-style configs (< 1.11.0)

scheduler:
  # class: "scheduler.periodic.PeriodicScheduler"
  infer_every: "1m"
  fit_every: "2m"
  fit_window: "3h"
...

will be implicitly converted to

schedulers:
  default_scheduler:  # default scheduler alias, backward compatibility
    # class: "scheduler.periodic.PeriodicScheduler"
    infer_every: "1m"
    fit_every: "2m"
    fit_window: "3h"
...

Parameters

class: str, default="scheduler.periodic.PeriodicScheduler", options={"scheduler.periodic.PeriodicScheduler", "scheduler.oneoff.OneoffScheduler", "scheduler.backtesting.BacktestingScheduler"}

• "scheduler.periodic.PeriodicScheduler": periodically runs the models on new data. Useful for consecutive re-trainings to counter data drift and model degradation over time.
• "scheduler.oneoff.OneoffScheduler": runs the process once and exits. Useful for testing.
• "scheduler.backtesting.BacktestingScheduler": imitates consecutive backtesting runs of OneoffScheduler. Runs the process once and exits. Use to get more granular control over testing on historical data.

Depending on selected class, different parameters should be used

Periodic scheduler

Parameters

For periodic scheduler parameters are defined as differences in times, expressed in difference units, e.g. days, hours, minutes, seconds.

Examples: "50s", "4m", "3h", "2d", "1w".

	Time granularity
s	seconds
m	minutes
h	hours
d	days
w	weeks

Parameter	Type	Example	Description
fit_window	str	"14d"	What time range to use for training the models. Must be at least 1 second.
infer_every	str	"1m"	How often a model will write its conclusions on newly added data. Must be at least 1 second.
fit_every	str, Optional	"1h"	How often to completely retrain the models. If missing value of infer_every is used and retrain on every inference run.

Periodic scheduler config example

scheduler:
  class: "scheduler.periodic.PeriodicScheduler"
  fit_window: "14d" 
  infer_every: "1m" 
  fit_every: "1h" 

This part of the config means that vmanomaly will calculate the time window of the previous 14 days and use it to train a model. Every hour model will be retrained again on 14 days’ data, which will include + 1 hour of new data. The time window is strictly the same 14 days and doesn't extend for the next retrains. Every minute vmanomaly will produce model inferences for newly added data points by using the model that is kept in memory at that time.

Oneoff scheduler

Parameters

For Oneoff scheduler timeframes can be defined in Unix time in seconds or ISO 8601 string format. ISO format supported time zone offset formats are:

• Z (UTC)
• ±HH:MM
• ±HHMM
• ±HH

If a time zone is omitted, a timezone-naive datetime is used.

Defining fitting timeframe

Format	Parameter	Type	Example	Description
ISO 8601	fit_start_iso	str	"2022-04-01T00:00:00Z", "2022-04-01T00:00:00+01:00", "2022-04-01T00:00:00+0100", "2022-04-01T00:00:00+01"	Start datetime to use for training a model. ISO string or UNIX time in seconds.
UNIX time	fit_start_s	float	1648771200
ISO 8601	fit_end_iso	str	"2022-04-10T00:00:00Z", "2022-04-10T00:00:00+01:00", "2022-04-10T00:00:00+0100", "2022-04-10T00:00:00+01"	End datetime to use for training a model. Must be greater than fit_start_*. ISO string or UNIX time in seconds.
UNIX time	fit_end_s	float	1649548800

Defining inference timeframe

Format	Parameter	Type	Example	Description
ISO 8601	infer_start_iso	str	"2022-04-11T00:00:00Z", "2022-04-11T00:00:00+01:00", "2022-04-11T00:00:00+0100", "2022-04-11T00:00:00+01"	Start datetime to use for a model inference. ISO string or UNIX time in seconds.
UNIX time	infer_start_s	float	1649635200
ISO 8601	infer_end_iso	str	"2022-04-14T00:00:00Z", "2022-04-14T00:00:00+01:00", "2022-04-14T00:00:00+0100", "2022-04-14T00:00:00+01"	End datetime to use for a model inference. Must be greater than infer_start_*. ISO string or UNIX time in seconds.
UNIX time	infer_end_s	float	1649894400

ISO format scheduler config example

scheduler:
 class: "scheduler.oneoff.OneoffScheduler"
 fit_start_iso: "2022-04-01T00:00:00Z"
 fit_end_iso: "2022-04-10T00:00:00Z"
 infer_start_iso: "2022-04-11T00:00:00Z"
 infer_end_iso: "2022-04-14T00:00:00Z"

UNIX time format scheduler config example

scheduler:
 class: "scheduler.oneoff.OneoffScheduler"
 fit_start_iso: 1648771200
 fit_end_iso: 1649548800
 infer_start_iso: 1649635200
 infer_end_iso: 1649894400

Backtesting scheduler

Parameters

As for Oneoff scheduler, timeframes can be defined in Unix time in seconds or ISO 8601 string format. ISO format supported time zone offset formats are:

• Z (UTC)
• ±HH:MM
• ±HHMM
• ±HH

If a time zone is omitted, a timezone-naive datetime is used.

Defining overall timeframe

This timeframe will be used for slicing on intervals (fit_window, infer_window == fit_every), starting from the latest available time point, which is to_* and going back, until no full fit_window + infer_window interval exists within the provided timeframe.

Format	Parameter	Type	Example	Description
ISO 8601	from_iso	str	"2022-04-01T00:00:00Z", "2022-04-01T00:00:00+01:00", "2022-04-01T00:00:00+0100", "2022-04-01T00:00:00+01"	Start datetime to use for backtesting.
UNIX time	from_s	float	1648771200
ISO 8601	to_iso	str	"2022-04-10T00:00:00Z", "2022-04-10T00:00:00+01:00", "2022-04-10T00:00:00+0100", "2022-04-10T00:00:00+01"	End datetime to use for backtesting. Must be greater than from_start_*.
UNIX time	to_s	float	1649548800

Defining training timeframe

The same explicit logic as in Periodic scheduler

Format	Parameter	Type	Example	Description
ISO 8601	fit_window	str	"PT1M", "P1H"	What time range to use for training the models. Must be at least 1 second.
Prometheus-compatible			"1m", "1h"

Defining inference timeframe

In BacktestingScheduler, the inference window is implicitly defined as a period between 2 consecutive model fit_every runs. The latest inference window starts from to_s - fit_every and ends on the latest available time point, which is to_s. The previous periods for fit/infer are defined the same way, by shifting fit_every seconds backwards until we get the last full fit period of fit_window size, which start is >= from_s.

Format	Parameter	Type	Example	Description
ISO 8601	fit_every	str	"PT1M", "P1H"	What time range to use previously trained model to infer on new data until next retrain happens.
Prometheus-compatible			"1m", "1h"

ISO format scheduler config example

scheduler:
 class: "scheduler.backtesting.BacktestingScheduler"
 from_iso: '2021-01-01T00:00:00Z'
 to_iso: '2021-01-14T00:00:00Z'
 fit_window: 'P14D'
 fit_every: 'PT1H'

UNIX time format scheduler config example

scheduler:
 class: "scheduler.backtesting.BacktestingScheduler"
 from_s: 167253120
 to_s: 167443200
 fit_window: '14d'
 fit_every: '1h'