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136 lines
5.2 KiB
Markdown
---
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sort: 11
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weight: 11
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title: vmanomaly
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menu:
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docs:
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parent: "victoriametrics"
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weight: 11
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aliases:
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- /vmanomaly.html
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---
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# vmanomaly
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**_vmanomaly is a part of [enterprise package](https://docs.victoriametrics.com/enterprise.html).
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Please [contact us](https://victoriametrics.com/contact-us/) to find out more._**
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## About
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**VictoriaMetrics Anomaly Detection** is a service that continuously scans Victoria Metrics time
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series and detects unexpected changes within data patterns in real-time. It does so by utilizing
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user-configurable machine learning models.
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It periodically queries user-specified metrics, computes an “anomaly score” for them, based on how
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well they fit a predicted distribution, taking into account periodical data patterns with trends,
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and pushes back the computed “anomaly score” to Victoria Metrics. Then, users can enable alerting
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rules based on the “anomaly score”.
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Compared to classical alerting rules, anomaly detection is more “hands-off” i.e. it allows users to
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avoid setting up manual alerting rules set up and catching anomalies that were not expected to happen.
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In other words, by setting up alerting rules, a user must know what to look for, ahead of time,
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while anomaly detection looks for any deviations from past behavior.
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In addition to that, setting up alerting rules manually has been proven to be tedious and
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error-prone, while anomaly detection can be easier to set up, and use the same model for different
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metrics.
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## How?
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Victoria Metrics Anomaly Detection service (**vmanomaly**) allows you to apply several built-in
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anomaly detection algorithms. You can also plug in your own detection models, code doesn’t make any
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distinction between built-in models or external ones.
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All the service parameters (model, schedule, input-output) are defined in a config file.
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Single config file supports only one model, but it’s totally OK to run multiple **vmanomaly**
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processes in parallel, each using its own config.
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## Models
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Currently, vmanomaly ships with a few common models:
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1. **ZScore**
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_(useful for testing)_
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Simplistic model, that detects outliers as all the points that lie farther than a certain amount
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from time-series mean (straight line). Keeps only two model parameters internally:
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`mean` and `std` (standard deviation).
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2. **Prophet**
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_(simplest in configuration, recommended for getting starting)_
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Uses Facebook Prophet for forecasting. The _anomaly score_ is computed of how close the actual time
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series values follow the forecasted values (_yhat_), and whether it’s within forecasted bounds
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(_yhat_lower_, _yhat_upper_). The _anomaly score_ reaches 1.0 if the actual data values
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are equal to
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_yhat_lower_ or _yhat_upper_. The _anomaly score_ is above 1.0 if the actual data values are
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outside
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the _yhat_lower_/_yhat_upper_ bounds.
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See [Prophet documentation](https://facebook.github.io/prophet/)
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3. **Holt-Winters**
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Very popular forecasting algorithm. See [statsmodels.org documentation](
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https://www.statsmodels.org/stable/generated/statsmodels.tsa.holtwinters.ExponentialSmoothing.html)
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for Holt-Winters exponential smoothing.
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4. **Seasonal-Trend Decomposition**
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Extracts three components: season, trend, and residual, that can be plotted individually for
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easier debugging. Uses LOESS (locally estimated scatterplot smoothing).
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See [statsmodels.org documentation](https://www.statsmodels.org/dev/examples/notebooks/generated/stl_decomposition.html)
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for LOESS STD.
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5. **ARIMA**
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Commonly used forecasting model. See [statsmodels.org documentation](https://www.statsmodels.org/stable/generated/statsmodels.tsa.arima.model.ARIMA.html) for ARIMA.
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6. **Rolling Quantile**
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A simple moving window of quantiles. Easy to use, easy to understand, but not as powerful as
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other models.
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### Examples
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For example, here’s how Prophet predictions could look like on a real-data example
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(Prophet auto-detected seasonality interval):
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And here’s what Holt-Winters predictions real-world data could look like (seasonality manually
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set to 1 week). Notice that it predicts anomalies in
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different places than Prophet because the model noticed there are usually spikes on Friday
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morning, so it accounted for that:
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## Process
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Upon starting, vmanomaly queries the initial range of data, and trains its model (“fit” by convention).
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Then, reads new data from VictoriaMetrics, according to schedule, and invokes its model to compute
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“anomaly score” for each data point. The anomaly score ranges from 0 to positive infinity.
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Values less than 1.0 are considered “not an anomaly”, values greater or equal than 1.0 are
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considered “anomalous”, with greater values corresponding to larger anomaly.
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Then, VMAnomaly pushes the metric to vminsert (under the user-configured metric name,
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optionally preserving labels).
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## Usage
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The vmanomaly accepts only one parameter -- config file path:
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```sh
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python3 vmanomaly.py config_zscore.yaml
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```
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or
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```sh
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python3 -m vmanomaly config_zscore.yaml
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```
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It is also possible to split up config into multiple files, just list them all in the command line:
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```sh
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python3 -m vmanomaly model_prophet.yaml io_csv.yaml scheduler_oneoff.yaml
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```
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