> **Note: Starting from [v1.11.0](/anomaly-detection/changelog#v1110) scheduler section in config supports multiple schedulers via aliasing. <br>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.**
-`"scheduler.periodic.PeriodicScheduler"`: periodically runs the models on new data. Useful for consecutive re-trainings to counter [data drift](https://www.datacamp.com/tutorial/understanding-data-drift-model-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.
<td>What time range to use for training the models. Must be at least 1 second.</td>
</tr>
<tr>
<td><code>infer_every</code></td>
<td>str</td>
<td><code>"1m"</code></td>
<td>How often a model will write its conclusions on newly added data. Must be at least 1 second.</td>
</tr>
<tr>
<td><code>fit_every</code></td>
<td>str, Optional</td>
<td><code>"1h"</code></td>
<td>How often to completely retrain the models. If missing value of <code>infer_every</code> is used and retrain on every inference run.</td>
</tr>
</tbody>
</table>
### Periodic scheduler config example
```yaml
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.
<tdrowspan=2>End datetime to use for a model inference. Must be greater than <code>infer_start_*</code>. ISO string or UNIX time in seconds.</td>
</tr>
<tr>
<td>UNIX time</td>
<td><code>infer_end_s</code></td>
<td>float</td>
<td>1649894400</td>
</tr>
</tbody>
</table>
### ISO format scheduler config example
```yaml
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
```yaml
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](#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.
<tdrowspan=2>End datetime to use for backtesting. Must be greater than <code>from_start_*</code>.</td>
</tr>
<tr>
<td>UNIX time</td>
<td><code>to_s</code></td>
<td>float</td>
<td>1649548800</td>
</tr>
</tbody>
</table>
### Defining training timeframe
The same *explicit* logic as in [Periodic scheduler](#periodic-scheduler)
<table>
<thead>
<tr>
<th>Format</th>
<th>Parameter</th>
<th>Type</th>
<th>Example</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>ISO 8601</td>
<tdrowspan=2><code>fit_window</code></td>
<tdrowspan=2>str</td>
<td><code>"PT1M", "P1H"</code></td>
<tdrowspan=2>What time range to use for training the models. Must be at least 1 second.</td>
</tr>
<tr>
<td>Prometheus-compatible</td>
<td><code>"1m", "1h"</code></td>
</tr>
</tbody>
</table>
### 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`.
<table>
<thead>
<tr>
<th>Format</th>
<th>Parameter</th>
<th>Type</th>
<th>Example</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>ISO 8601</td>
<tdrowspan=2><code>fit_every</code></td>
<tdrowspan=2>str</td>
<td><code>"PT1M", "P1H"</code></td>
<tdrowspan=2>What time range to use previously trained model to infer on new data until next retrain happens.</td>
