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

weight	title	menu	aliases
1	Built-in Models	docs parent weight vmanomaly-models 1	/anomaly-detection/components/models/models.html

Models config parameters

Section Overview

VM Anomaly Detection (vmanomaly hereinafter) models support 2 groups of parameters:

• vmanomaly-specific arguments - please refer to Parameters specific for vmanomaly and Default model parameters subsections for each of the models below.
• Arguments to inner model (say, Facebook's Prophet), passed in a args argument as key-value pairs, that will be directly given to the model during initialization to allow granular control. Optional.

Note: For users who may not be familiar with Python data types such as list[dict], a dictionary in Python is a data structure that stores data values in key-value pairs. This structure allows for efficient data retrieval and management.

Models:

• ARIMA
• Holt-Winters
• Prophet
• Rolling Quantile
• Seasonal Trend Decomposition
• Z-score
• MAD (Median Absolute Deviation)
• Isolation forest (Multivariate)
• Custom model

ARIMA

Here we use ARIMA implementation from statsmodels library

Parameters specific for vmanomaly:

• class (string) - model class name "model.arima.ArimaModel"

• z_threshold (float, optional) - standard score for calculating boundaries to define anomaly score. Defaults to 2.5.

• provide_series (list[string], optional) - List of columns to be produced and returned by the model. Defaults to ["anomaly_score", "yhat", "yhat_lower" "yhat_upper", "y"]. Output can be only a subset of a given column list.

• resample_freq (string, optional) - Frequency to resample input data into, e.g. data comes at 15 seconds resolution, and resample_freq is '1m'. Then fitting data will be downsampled to '1m' and internal model is trained at '1m' intervals. So, during inference, prediction data would be produced at '1m' intervals, but interpolated to "15s" to match with expected output, as output data must have the same timestamps.

Default model parameters:

• order (list[int]) - ARIMA's (p,d,q) order of the model for the autoregressive, differences, and moving average components, respectively.

• args (dict, optional) - Inner model args (key-value pairs). See accepted params in model documentation. Defaults to empty (not provided). Example: {"trend": "c"}

Config Example

model:
  class: "model.arima.ArimaModel"
  # ARIMA's (p,d,q) order
  order: [1, 1, 0] 
  z_threshold: 2.7
  resample_freq: '1m'
  # Inner model args (key-value pairs) accepted by statsmodels.tsa.arima.model.ARIMA
  args:
    trend: 'c'

Holt-Winters

Here we use Holt-Winters Exponential Smoothing implementation from statsmodels library. All parameters from this library can be passed to the model.

Parameters specific for vmanomaly:

• class (string) - model class name "model.holtwinters.HoltWinters"

• frequency (string) - Must be set equal to sampling_period. Model needs to know expected data-points frequency (e.g. '10m'). If omitted, frequency is guessed during fitting as the median of intervals between fitting data timestamps. During inference, if incoming data doesn't have the same frequency, then it will be interpolated. E.g. data comes at 15 seconds resolution, and our resample_freq is '1m'. Then fitting data will be downsampled to '1m' and internal model is trained at '1m' intervals. So, during inference, prediction data would be produced at '1m' intervals, but interpolated to "15s" to match with expected output, as output data must have the same timestamps. As accepted by pandas.Timedelta (e.g. '5m').

• seasonality (string, optional) - As accepted by pandas.Timedelta.

If seasonal_periods is not specified, it is calculated as seasonality / frequency Used to compute "seasonal_periods" param for the model (e.g. '1D' or '1W').

• z_threshold (float, optional) - standard score for calculating boundaries to define anomaly score. Defaults to 2.5.

Default model parameters:

• If parameter seasonal is not specified, default value will be add.

• If parameter initialization_method is not specified, default value will be estimated.

• args (dict, optional) - Inner model args (key-value pairs). See accepted params in model documentation. Defaults to empty (not provided). Example: {"seasonal": "add", "initialization_method": "estimated"}

Config Example

model:
  class: "model.holtwinters.HoltWinters"
  seasonality: '1d'
  frequency: '1h'
  # Inner model args (key-value pairs) accepted by statsmodels.tsa.holtwinters.ExponentialSmoothing
  args:
    seasonal: 'add'
    initialization_method: 'estimated'

Resulting metrics of the model are described here.

Prophet

Here we utilize the Facebook Prophet implementation, as detailed in their library documentation. All parameters from this library are compatible and can be passed to the model.

Parameters specific for vmanomaly:

• class (string) - model class name "model.prophet.ProphetModel"
• seasonalities (list[dict], optional) - Extra seasonalities to pass to Prophet. See add_seasonality() Prophet param.
• provide_series (dict, optional) - model resulting metrics. If not specified standard metrics will be provided.

Note: Apart from standard vmanomaly output Prophet model can provide additional metrics.

Additional output metrics produced by FB Prophet Depending on chosen seasonality parameter FB Prophet can return additional metrics such as:

• trend, trend_lower, trend_upper
• additive_terms, additive_terms_lower, additive_terms_upper,
• multiplicative_terms, multiplicative_terms_lower, multiplicative_terms_upper,
• daily, daily_lower, daily_upper,
• hourly, hourly_lower, hourly_upper,
• holidays, holidays_lower, holidays_upper,
• and a number of columns for each holiday if holidays param is set

Config Example

model:
  class: "model.prophet.ProphetModel"
  seasonalities:
    - name: 'hourly'
      period: 0.04166666666
      fourier_order: 30
  # Inner model args (key-value pairs) accepted by
  # https://facebook.github.io/prophet/docs/quick_start.html#python-api
  args:
    # See https://facebook.github.io/prophet/docs/uncertainty_intervals.html
    interval_width: 0.98
    country_holidays: 'US'

Resulting metrics of the model are described here

Rolling Quantile

Parameters specific for vmanomaly:

• class (string) - model class name "model.rolling_quantile.RollingQuantileModel"
• quantile (float) - quantile value, from 0.5 to 1.0. This constraint is implied by 2-sided confidence interval.
• window_steps (integer) - size of the moving window. (see 'sampling_period')

Config Example

model:
  class: "model.rolling_quantile.RollingQuantileModel"
  quantile: 0.9
  window_steps: 96

Resulting metrics of the model are described here.

Seasonal Trend Decomposition

Here we use Seasonal Decompose implementation from statsmodels library. Parameters from this library can be passed to the model. Some parameters are specifically predefined in vmanomaly and can't be changed by user(model='additive', two_sided=False).

Parameters specific for vmanomaly:

• class (string) - model class name "model.std.StdModel"
• period (integer) - Number of datapoints in one season.
• z_threshold (float, optional) - standard score for calculating boundaries to define anomaly score. Defaults to 2.5.

Config Example

model:
  class: "model.std.StdModel"
  period: 2

Resulting metrics of the model are described here.

Additional output metrics produced by Seasonal Trend Decomposition model

• resid - The residual component of the data series.
• trend - The trend component of the data series.
• seasonal - The seasonal component of the data series.

MAD (Median Absolute Deviation)

The MAD model is a robust method for anomaly detection that is less sensitive to outliers in data compared to standard deviation-based models. It considers a point as an anomaly if the absolute deviation from the median is significantly large.

Parameters specific for vmanomaly:

• class (string) - model class name "model.mad.MADModel"
• threshold (float, optional) - The threshold multiplier for the MAD to determine anomalies. Defaults to 2.5. Higher values will identify fewer points as anomalies.

Config Example

model:
  class: "model.mad.MADModel"
  threshold: 2.5

Resulting metrics of the model are described here.

Z-score

Parameters specific for vmanomaly:

• class (string) - model class name "model.zscore.ZscoreModel"
• z_threshold (float, optional) - standard score for calculation boundaries and anomaly score. Defaults to 2.5.

Config Example

model:
  class: "model.zscore.ZscoreModel"
  z_threshold: 2.5

Resulting metrics of the model are described here.

Isolation forest (Multivariate)

Detects anomalies using binary trees. The algorithm has a linear time complexity and a low memory requirement, which works well with high-volume data. It can be used on both univatiate and multivariate data, but it is more effective in multivariate case.

Important: Be aware of the curse of dimensionality. Don't use single multivariate model if you expect your queries to return many time series of less datapoints that the number of metrics. In such case it is hard for a model to learn meaningful dependencies from too sparse data hypercube.

Here we use Isolation Forest implementation from scikit-learn library. All parameters from this library can be passed to the model.

Parameters specific for vmanomaly:

• class (string) - model class name "model.isolation_forest.IsolationForestMultivariateModel"

• contamination (float or string, optional) - The amount of contamination of the data set, i.e. the proportion of outliers in the data set. Used when fitting to define the threshold on the scores of the samples. Default value - "auto". Should be either "auto" or be in the range (0.0, 0.5].

• args (dict, optional) - Inner model args (key-value pairs). See accepted params in model documentation. Defaults to empty (not provided). Example: {"random_state": 42, "n_estimators": 100}

Config Example

model:
  # To use univariate model, substitute class argument with "model.isolation_forest.IsolationForestModel".
  class: "model.isolation_forest.IsolationForestMultivariateModel"
  contamination: "auto"
  args:
    n_estimators: 100
    # i.e. to assure reproducibility of produced results each time model is fit on the same input
    random_state: 42

Resulting metrics of the model are described here.

Custom model

You can find a guide on setting up a custom model here.

vmanomaly output

When vmanomaly is executed, it generates various metrics, the specifics of which depend on the model employed. These metrics can be renamed in the writer's section.

The default metrics produced by vmanomaly include:

• anomaly_score: This is the primary metric.

  • It is designed in such a way that values from 0.0 to 1.0 indicate non-anomalous data.
  • A value greater than 1.0 is generally classified as an anomaly, although this threshold can be adjusted in the alerting configuration.
  • The decision to set the changepoint at 1 was made to ensure consistency across various models and alerting configurations, such that a score above 1 consistently signifies an anomaly.

• yhat: This represents the predicted expected value.

• yhat_lower: This indicates the predicted lower boundary.

• yhat_upper: This refers to the predicted upper boundary.

• y: This is the original value obtained from the query result.

Important: Be aware that if NaN (Not a Number) or Inf (Infinity) values are present in the input data during infer model calls, the model will produce NaN as the anomaly_score for these particular instances.

Healthcheck metrics

Each model exposes several healthchecks metrics to its health_path endpoint: